Development and psychometric properties of a medication information literacy scale for Chinese pregnant women

doi:10.21203/rs.3.rs-2981039/v1

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Development and psychometric properties of a medication information literacy scale for Chinese pregnant women

https://doi.org/10.21203/rs.3.rs-2981039/v1

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Background

Pregnant women are in a unique physiological state and the safety of medication is an important issue related to maternal and infant health. With the increasing variety of information resources, the ability of pregnant women to correctly understand and make decisions about medication information depends upon their level of medication information literacy. However, currently, there is no valid and reliable instrument to measure this concept. The purpose of this study was to develop a medication information literacy scale for use in Chinese pregnant women and to measure their medication information literacy.

Methods

Under the guidance of the Ellis’ Information-Seeking Model and the Comprehensive Model of Information Seeking, preliminary items were developed through a literature review, expert consultation and modification. Following a pilot study, items were selected or revised using item discrimination, item correlation analysis and feedback from the participants. The final version of the medication information literacy scale was reviewed by 731 women to establish reliability and validity.

Results

The final scale consisted of five domains with 22 items. The Cronbach’s α coefficient was 0.871 and test-retest reliability was 0.870. Total explained variation was 64.642%. The scale demonstrated good validity (χ²/df = 1.999, GFI = 0.899, AGFI = 0.871, SRMR = 0.046, IFI = 0.926, RMSEA = 0.054, CFI = 0.925, and TLI = 0.913). In a formal survey of pregnant women, only 27.9% of pregnant women had adequate medication information literacy.

Conclusion

The medication information literacy scale demonstrates good reliability and validity, and can be used to assess medication information literary of pregnant women in Chinese context, thus leading to targeted interventions for this unique population.

Medication information literacy

Pregnant women

Scale

Reliability

Validity

The safe use of medications is a major focus in public health, especially for pregnant women [1]. Due to the physiological and metabolic changes associated with pregnancy which directly affect drug absorption, distribution, metabolism and excretion (ADME), and issues related to medical ethics, more than 90% of medications lack pharmacokinetic data on use in pregnant women [2, 3]. Moreover, the World Health Organization (WHO) reported that 86% of pregnant women had to take medicine for acute or chronic diseases (e.g. gestational hypertension, gestational diabetes, depression, or high fever), and it was most common in the first trimester [4].

Drug exposure during pregnancy may lead to adverse maternal or fetal outcomes (e.g. cumulative toxicosis of pregnant women, birth defects, preterm birth) and has long-term effects on infant development [5–7]. Most drug-related adverse events are due to misunderstanding of medication information. That may be caused by the instructions which contain a large amount of terminology that is difficult for most pregnant women to understand [8]. In addition, the quality of this information is not guaranteed depending upon the source used.

Medication compliance of pregnant women has been found to be generally poor [9], especially related to long-term drug treatment of chronic diseases [10]. Previous surveys have shown that pregnant women tended to overestimate the teratogenic risk of medication and were less willing to use medication during pregnancy [9, 11, 12]. This might result in an optional drug withdrawal leading to potential disease relapse [13–15]. Safe and effective use of medications depends not only on the understanding of warnings or contraindications but also on adherence to the instructions [16]. Correct understanding of medication information can help ease anxiety in pregnant women and improve their compliance with appropriate medication.

According to reports, the main sources of medical information for pregnant women were clinicians, online platforms, friends and family, and written materials (such as nonofficial pharmacopoeias, health handbooks or drug handbooks) [17, 18]. In China, an unbalanced doctor-patient ratio has made it difficult for doctors to ensure that pregnant women are given adequate medication guidance [19], and the COVID-19 pandemic has forced pregnant women to reduce the frequency of medical visits to avoid infection [20]. Instead, the Internet has become the most frequent and fastest way for women to obtain medication information because of its accessibility and ubiquity [21]. A previous study reported that 60% of patients depended heavily on web information [22]. The Internet provides abundant medication information but its quality has been difficult to control [23]. An individual’s level of health literacy (HL) is considered to be related to one's level of medication knowledge, medication adherence, health outcomes, and encounters of medication error^,[24, 25]. For pregnant women with low levels of HL, it is difficult to independently judge the authenticity of the medication information [26]. Notably, they not only need an ability to obtain appropriate information, but to also interpret it correctly and effectively distinguish its authenticity, which is influenced by medication information literacy (MIL) to a large extent.

Medication information literacy (MIL) combining medication literacy (ML) and information literacy (IL), includes: actively identifying the need for medication information; efficiently obtaining the necessary information; accurate comprehension of instructions; critically appraise of the information; and use of it to make informed medication-related decisions [27]. The MIL differs from general HL in that it is functional and represents an individual's ability to identify, access, understand, evaluate, integrate and apply information, and also includes the ability to exchange medication information, calculate dosages and use medication data to make informed health decisions [19]. A lower level of MIL of pregnant women might be a predictor of adverse drug-related events [16].

Despite the importance of MIL for the safe use of medication, most existing instruments are used for general HL measurement or designed for testing the HL of specific diseases or specific medications, with few specifically designed for MIL aimed at pregnant women [17, 28, 29]. Due to the particularity and sensitivity of pregnancy, pregnant women pay more attention to medication information than other groups [1]. Adequate MIL is of great significance for pregnant women to seek healthcare service effectively and participate in medical decision-making actively. To assess MIL levels of pregnant women and subsequently create a supportive environment that encourages correct use of medications, it is essential to develop a valid and reliable MIL tool. As a result, researchers in the current study developed and validated a medication information literacy scale (MILS) for pregnant women in a Chinese context, which will be used to identify pregnant women with low levels of MIL, and is expected to establish a targeted intervention program to promote effective and safe medication therapy.

Study design

This is a psychometric study to develop and validate a MILS for pregnant women to measure their MIL. The design process involved four stages: selection of appropriate indicators (stage 1); scale development (stage 2); pilot survey (stage 3) and psychometric testing (stage 4). Approval for this research was obtained from the Research Ethics Committee of the authors’ institution and the associated hospitals.

Theoretical framework

The Comprehensive Model of Information Seeking (CMIS) takes into account motivational variables for health-related information seeking [30]. The CMIS consists of three parts: (1) antecedents such as demographics, direct experience (the degree of personal experience with disease), salience (the personal significance of disease-related information), and beliefs (an individual’s belief in the efficacy of various medical procedures); (2) information carrier factors such as characteristics (an individual’s perceptions about carriers’ characteristics), and utilities (an individual’s utilization of the all interpersonal network and mediated communication channels); and (3) information seeking actions [30]. The antecedent factors provide the motivation for information seeking, and the motivations are affected and limited by information carrier factors. CMIS is an effective model to predict people’s health information seeking and can be applied in designing health interventions [31].

Ellis’ Information-Seeking Behavior Model is a complete presentation of the information seeking process, which used features to describe the various stages of the process [32]. Ellis concluded eight main features of information-seeking process:(1) Starting; (2) Chaining; (3) Browsing; (4) Differentiating; (5) Monitoring; (6) Extracting; (7) Verifying; and (8) Ending. It divides the user's search behaviors for a certain kind of information into different observable periods, which facilitates the analysis and integration of the user's search behaviors and cognitive needs [33].

According to the CMIS, the medication information-seeking behavior of pregnant women is influenced by their demographic characteristics, direct experience, personal relevance factors, and information carriers. The CMIS model emphasizes that the accessibility of the information carriers is even more important than the quality of the information provided by the carrier. Research has shown that the top three sources of medication information for pregnant women were doctors (73%), the Internet (60%), and pharmacists (46%) [34]. In China the Internet has become the most common tool for expectant mothers to solve problems and obtain answers to queries during pregnancy [35]. This model highlights the importance of information media in the process of searching for medication information. Meanwhile, the process of seeking medication information of pregnant women follows the features of Ellis’ Information-Seeking Behavior Model. Therefore, the measurement of MIL of pregnant women requires integrating various factors influencing medication information seeking into different stages of the medication information search process. Subsequently, the researchers identified the following areas of specific content for inclusion in the scale: awareness of finding medication information, sources of medication information, assessment of the quality of medication information, cognition of medication information, and medication-taking behavior. (Fig. 1)

Figure 1. Medication information literacy framework

Instrument development

Stage 1: selection of appropriate indicators

Items were generated by conducting a systematic literature review of databases (Cochrane Library, PubMed, Web of Science, Embase, CINAHL, China National Knowledge Infrastructure, and Wan Fang Database). Thirteen articles were identified, after removing all duplicates, screening titles and abstracts, and accessing full-text articles. Based on medication information literacy framework and by analyzing these 13 articles, an initial draft of a scale with 35 items was developed and a priori themes for the 35 items were set: medication information needs; medication information source; medication information quality discrimination; medication information source awareness; and medication-taking behavior.

Stage 2: Scale development

The initial draft of the scale with 35 items was sent to ten experts for their review and feedback. Five experts were engaged in nursing management (20%) and nursing education (30%) for more than ten years, and the remaining experts included midwives (20%), obstetricians (10%), and pharmacists (20%) from a tertiary hospital. The preliminary assessment was conducted on the basis of the experts' professions, the positive coefficient of these experts, and their degree of familiarity with the content, in order to judge whether the selected experts met the requirements of the study and to reduce selection bias. There were ten experts meeting the criteria.

An evaluation form describing the purpose and significance of the scale development combing 35 items and 5 priori themes were sent to the expert group. The experts need to assess the importance of each item in four possible levels: The first level was considered very relevant; the second level was relevant but needed to be modified; the third level must be modified, otherwise it was irrelevant; and the fourth level was considered irrelevant. Items deemed to be first or second level were retained after some modification. Further, items deemed third level were either retained, deleted, or merged after discussion and modification. Finally, items considered to be fourth level were deleted from the measure. Moreover, experts were asked to give their recommendations about the modification after evaluating at end of the evaluation form.

The study determined the content validity of the scale according to the degree of concentration and coordination of each item's opinions from the experts, combined with the experts' opinions on modification. After two rounds of consultation with these experts, their constructive amendments and suggestions were integrated and synthesized based on content representativeness, relevance, feasibility and significance. As a result, four items in the proposed scale were enhanced, two were merged, four were deleted and six were modified. Thirty-four items formed the preliminary Medication information literacy Scale (MILS).

Stage 3: Pilot survey

To test content comprehension and item discrimination of the scale, a purposive sample of 117 pregnant women was recruited from a tertiary hospital in Wuhan, China. Inclusion criteria were: (a) pregnant female; (b) able to read and speak Mandarin; (c) able to complete the questionnaire independently; and (d) voluntary participation in the pilot survey. Participants were surveyed using the preliminary MILS and encouraged to express their opinion regarding item ambiguity and lack of clarity. A total of 117 questionnaires were returned and 13 were deleted due to their being incomplete (effective return rate of 88.89%). As a result, four items were revised based on participants’ comments. Item discrimination analysis was calculated to identify item sensitivity. Valid questionnaires were ranked in order from the highest score group (top 27%) to the lowest group (bottom 27%). T-test was used to detect the difference of each item between the two groups and results showed that there were two items with no significant difference. Inter-item and item-total correlations were checked. Four items with a correlation coefficient of r < 0.3 (P < 0.05) were found. Considering the accuracy of the scale, two items meeting the deletion criteria of the two methods were deleted. The MILS was then revised to include 32 valid items.

The preliminary MILS consisting of 32 items and 5 main dimensions was a 5-point Likert scale. Each item’s score is ranging from 1 to 5 (1 = seldom true, 2 = occasionally true, 3 = often true, 4 = usually true, and 5 = almost true). A total score was obtained by adding the score of each item (reverse items were scored inversely). The higher the score, the higher the level of MIL of pregnant women.

Stage 4: Psychometric testing of the MILS

A cross-sectional study was conducted to assess the MIL of the pregnant women. Participants were recruited from the obstetric clinics of two tertiary hospitals in Wuhan, using a convenience sample of women meeting the same criteria as participants in the pilot study. A sample size of 10 times the number of items in the scale, and considering an estimated 20% sample attrition, should require a minimum of 384 samples [36]. A total of 406 participants was recruited from the first hospital and 383 valid questionnaires were returned (response rate of 94.33%). A group of 377 participants was recruited from the second hospital and asked to complete questionnaires used for confirmatory factor analysis. A total of 348 valid questionnaires were obtained (response rate of 92.33%) which resulted in a total drop-out rate of 7%. A total of 731 women participated in this stage. Informed consent was obtained from all participants.

The psychometric property of the scale was assessed using reliability and validity analysis. Data from 731 participants were entered into SPSS (Statistic Package for Social Science, version 24.0) to measure internal consistency (Cronbach’s alpha). Two weeks later, electronic questionnaires were sent to 236 participants who agreed to the follow-up survey at enrollment, and 128 valid questionnaires were returned to access test-retest reliability. Exploratory factor analysis (EFA) was completed using a sample of the questionnaires from participants at the first hospital. Another sample of the 348 questionnaires from participants at the second hospital was used to complete confirmatory factor analysis (CFA). The EFA was conducted to explore the construct validity of the MILS and assess whether the data was grouped as anticipated. Principal component analysis (PCA) and extraction of principal factors can simplify the structure and capture the essence of the problem better on the basis of retaining most of the original information, which were completed on the 32 items using the maximum variance orthogonal rotation [36]. The Kaiser-Meyer-Olkin (KMO) and Bartlett’s Test of Sphericity were conducted to evaluate the accuracy of the sample for factor analysis [37, 38]. After modifying the scale based on the results of the EFA, CFA was used to determine the model fitting index of the MILS and verify the identified constructs [39]. Acceptable values of goodness-of-fit indexes were as follows: χ²/df (chi-square mean/degree of freedom ) ≤ 5; GFI (goodness-of-fit index), AGFI (adjusted goodness-of-fit index), IFI (incremental fit index), CFI (comparative fit index), and TLI (Tucker Lewis Index) ≥ 0.9; SRMR (standardized root mean square residual) ≤ 0.05; and RMSEA (root mean square error of approximation) ≤ 0.06 [36, 40–42].

Based on the trimesters of pregnancy, 731 pregnant women were divided into three groups (first trimester, second trimester, and third trimester). In addition, they were divided into three groups according to the level of education (primary school, secondary school, and university or above). Scores of MILS among three groups were compared with the overall scores to obtain the sensitivity of MILS in the trimesters of pregnancy and the level of education.

Socio-demographic characteristics of participants

Of the 783 distributed questionnaires, 731 were considered valid, yielding a response rate of 93%. As to the participants, the average age was 29.17 years (stand deviation [SD] = 5.67), average gestational weeks was 28.05 (SD = 6.80), and 62% were primipara. Characteristics of the respondents are shown in Table 1.

Table 1

Socio-demographic of participants (N = 731)
Measure	M (SD) / n (%)
Ethnicity
Han	694 (94.9)
Minority	37 (5.1)
Age (years)	29.17 ± 5.67
Education
Primary school	15 (2.1)
Secondary school	106 (14.5)
University or above	610 (83.4)
Location of residence
Urban	273 (37.3)
Rural	458 (62.7)
Occupation
Medical staff	64 (8.8)
Other occupation	667 (91.2)
Household monthly income (in RMB)
< 3000	32 (4.4)
3001–5000	149 (20.4)
5001–8000	264 (36.1)
> 8000	286 (39.1)
Gestation (weeks)	28.05 ± 6.80
Parity Primiparity Multiparity	453 (62.0) 278 (38.0)

Descriptive statistics of psychometric properties

Exploratory factor analysis (EFA)

From the 731 valid questionnaires, 383 were used to test the EFA. Moreover, the high KMO statistics (0.822) of the MILS suggested that the sample size was adequate for EFA (i.e., KMO > 0.7) [43], and significant Bartlett’s test of sphericity (p < 0.05) indicated that the items were sufficiently correlated [44]. Items, whose loading value being greater than 0.4 in one common factor and less than 0.4 in others, were regarded as the components of this common factor [45]. The load of factor of each item being greater than 0.6 is described in Table 2. The results of the EFA identified that there were five main domains with initial eigenvalues > 1, accounting for 64.642% of the total variation (Table 3).

Table 2

Rotated component matrix
	Factors
	1	2	3	4	5
Item 1		0.838
Item 2		0.842
Item 3		0.691
Item 4		0.799
Item 5			0.742
Item 6			0.771
Item 7			0.819
Item 8			0.731
Item 12					0.783
Item 13					0.846
Item 14					0.882
Item 15	0.783
Item 16	0.717
Item 17	0.794
Item 18	0.733
Item 19	0.842
Item 20	0.796
Item 21	0.738
Item 22				0.738
Item 26				0.815
Item 27				0.758
Item 28				0.780

Table 3

Principal component analysis
Element	Initial eigenvalues			Extraction sums of squared loadings			Rotation sums of squared loadings
Element	Total	Variance %	Accumulate %	Total	Variance %	Accumulate %	Total	Variance %	Accumulate %
1	5.287	24.030	24.030	5.287	24.030	24.030	4.305	19.570	19.570
2	3.007	13.667	37.697	3.007	13.667	37.697	2.629	11.950	31.520
3	2.336	10.618	48.314	2.336	10.618	48.314	2.499	11.360	42.880
4	2.017	9.167	57.481	2.017	9.167	57.481	2.489	11.315	54.194
5	1.575	7.161	64.642	1.575	7.161	64.642	2.299	10.448	64.642

Confirmatory factor analysis (CFA)

A total of 348 questionnaires were used to investigate the CFA of the five domains of the MILS. Chi-Square was 397.851. Values of GFI, AGFI, IFI, and CFI were 0.90, 0.872, 0.926, and 0.921, respectively. Values for SRMR and RMSEA were 0.048 and 0.054, respectively. Values of χ²/df and TLI were 2.014 and 0.908, respectively. All indices except GFI and AGFI, met the fitting requirements (Table 4). Although GFI and AGFI were less than the suggested threshold of 0.90, the result also indicated a relatively good fit. Structure equation modeling of the scale is shown in Fig. 2.

Table 4

Fit indicators of confirmatory factor analysis.
Parameters	Indicator level
χ²/df ^a	1.999 (< 5)
GFI ^b	0.899 (near to 0.9)
AGFI ^c	0.871 (near to 0.9)
SRMR ^d	0.046 (< 0.05)
IFI ^e	0.926 (＞0.9)
RMSEA ^f	0.054 (< 0.06)
CFI ^g	0.925 (＞0.9)
TLI ^h	0.913 (＞0.9)
^a χ²/df: chi-square mean/degree of freedom.
^b GFI: goodness-of-fit index.
^c AGFI: adjusted goodness-of-fit index.
^d SRMR: standardized root mean square residual.
^e IFI: incremental fit index.
^f RMSEA: root mean square error of approximation.
^g CFI: comparative fit index.
^h TLI: Tucker Lewis index.

Figure 2. Structure equation modeling of five domains with 22 items

1 = medication information needs; 2 = medication information sources; 3 = medication information quality discrimination; 4 = medication information source awareness; 5 = medication-taking behavior

Reliability

A total of 731 questionnaires was used to test the reliability of the MILS having 22 items. Cronbach’s alpha coefficient for the total scale was 0.871 and for the domains of medication information needs, medication information sources, medication information quality discrimination, medication information source awareness, and medication-taking behavior was 0.805, 0.776, 0.777, 0.867, and 0.784, respectively (Table 5). All Cronbach’s alpha coefficients were greater than 0.7, indicating good internal consistency and reliability [36]. Test-retest reliability coefficient for the overall scale was 0.870, and for each domain ranged from 0.725 to 0.879 (i.e., > 0.7), supporting reliability of the scale. Pearson’s correlation coefficients between the scale and each domain are shown in Table 6.

Table 5

Reliability coefficients of the total scale and each domain.
Domains	Items	Cronbach’s α coefficient	Test-retest reliability
Medication information needs	4	0.805	0.817
Medication information sources	4	0.776	0.879
Medication information quality discrimination	3	0.777	0.846
Medication information source awareness	7	0.867	0.725
Medication-taking behavior	4	0.784	0.866
The total score on MILS ^a	22	0.871	0.870
^a MILS indicates medication information literacy scale

Table 6

Correlation among domains and the total score.
Domains	1	2	3	4	5	Total
1	1
2	0.284	1
3	0.268	0.353	1
4	0.284	0.265	0.391	1
5	0.255	0.275	0.317	0.307	1
Total	0.637	0.623	0.630	0.764	0.625	1
1 = medication information needs; 2 = medication information sources; 3 = medication information quality discrimination; 4 = medication information source awareness; 5 = medication-taking behavior.

Sensitivity analysis

The MILS score of the whole participants (n = 731) was 79.76 (SD = 11.923). The independent sample t test was carried out between the score of MILS (67.33 ± 18.98) of Primary school (n = 15) and the overall score (t= -2.535, P > 0.01), the MILS score of the Secondary school(n = 106) was 76.00 ± 14.86 (t = -2.064, P > 0.01), and the MILS score of university or above (n = 610) was 80.72 ± 10.81 (t = 2.189, P > 0.01). Meanwhile, the independent sample t test was carried out between the score of MILS in three groups of pregnancy and the overall score. The MILS score in the early trimester (n = 9) was 75.44 ± 6.25 (t= -2.072, P > 0.01), the MILS score in the second trimester (n = 336) was 79.78 ± 12.09 (t = 0.030, P > 0.01) and the MILS score in the third trimester (n = 386) was79.84 ± 11.874 (t = 0.136, P > 0.01). Therefore, there were no statistical significances between the MILS scores in different level of education and different stage of pregnancy and the overall score (P > 0.01).

Medication information literacy score

The final MILS is shown in Table 7, having five domains with 22 items. Domain 1 was medication information needs (items 1, 2, 3, 4); domain 2 was medication information sources (items 5, 6, 7, 8); domain 3 was medication information quality discrimination (items 9, 10, 11); domain 4 was medication information source awareness (items 12, 13, 14, 15, 16, 17, 18); and domain 5 was medication-taking behavior (items19, 20, 21, 22). The threshold was set at 80% of the total score, based on a national survey of Chinese residents’ health literacy [46]. Pregnant women with scores below the threshold were considered to have insufficient MIL. The survey showed that only 27.9% of pregnant women had adequate medication information literacy (Table 8).

Table 7

Final version of the MILS.
Ordinal	Item
1	I have the awareness to look up medication information about pregnant women actively.
2	I think it is necessary to understand medication information about pregnant women.
3	I am worried that medication will have a bad effect on the fetus.
4	I am worried that medication will have a bad effect on me.
5	I obtain medication information from the Internet (such as websites, TV, Weibo, WeChat, etc.).
6	I obtain medication information from relatives and friends.
7	I obtain medication information from medication instructions, medication leaflets and medication books.
8	I obtain medication information from doctors, pharmacists and nurses.
9	I can find the medication information I need.
10	I am satisfied with the quality of the medication information I found.
11	The medication information I found answered my doubts.
12	I know that gestational earlier trimester is the sensitive period of medication.
13	I know that the risks of medication in different pregnancy trimester periods are varied.
14	I know the teratogenic effects of medications.
15	I know the difference between disallowed use and use with caution.
16	I think that taking medicine only based on online medication information is risky for the fetus.
17	I think that taking medicine only based on experience is risky for the fetus.
18	I think that taking medicine only based on medication books, instructions, etc. is risky for the fetus.
19	I comprehensively compare medication information from different sources.
20	I consult medical staff about medication information from the Internet.
21	I consult the medical staff about medication information from relatives and friends.
22	I consult the medical staff about the medication information from the medication book.

Table 8

Medication information literacy score.
		Rate (%)
Domains	Scores (mean ± SD)	Sufficient MIL	Insufficient MIL
Medication information needs	14.63 ± 3.37	42.5	57.5
Medication information sources	14.00 ± 3.26	31.7	68.3
Medication information quality discrimination	10.52 ± 2.25	35.7	64.3
Medication information source awareness	25.02 ± 5.30	36.4	63.6
Medication-taking behavior	15.59 ± 3.27	57.6	42.4
Total score on the MILS*	79.76 ± 11.92	27.9	72.1
*MILS: Medication Information Literacy Scale

This study developed and validated a 22-item medication information literacy scale (MILS) to assess the MIL of pregnant women, using a 5-point Likert scale with scores ranging from 22 to 110. The items covered five domains: medication information needs, medication information sources, medication information quality discrimination, medication information source awareness and medication-taking behavior. The MILS is designed to explore and quantify the ability of pregnant women to find, understand, evaluate and use medication information. The threshold was set at 80% of the total score [47]. The higher the score, the higher the level of MIL of pregnant women. The psychometric properties of the MILS were established in a Chinese context and showed good reliability and validity, which may accurately reflect the level of MIL in pregnant women. The MILS was developed comprehensively under the guidance of the conceptual framework (MIL) and a theoretical framework (Ellis’ Information-Seeking Behavior Model and the Comprehensive Model of Information Seeking). In the formal survey, the completion rate of the questionnaire was 93%, indicating high acceptability of the MILS in pregnant women.

The MILS was pilot tested involving 731 participants. Construct validity of the 22 item was good in the five domains which explained an accumulated 64.642% of the variance. The results of the principal component analysis revealed that all items had a factor loading greater than 0.6, further providing evidence of the factors solution [48]. The reliability coefficient of 0.871 was also good, denoting that the scale as a whole had internal consistency. The reliability of the MILS signifies dependability and consistency [49]. The validity and reliability of the MILS are vital as they enable health-care professions to feel confident that the respondent is competent with knowledge of medication use in pregnancy. According to the sensitivity analysis, there is no difference between the average score of MILS in the different level of education and in different stage of pregnancy and the overall average score. Therefore, MILS is suitable for pregnant women of any education level and any gestational age.

The medication information literacy (MIL) integrates information literacy(IL) and medication literacy(ML) [27]. The IL was defined as the ability of individuals to identify the type and scope of information required, efficiently access to needed information, critically evaluate the information and its sources, combine designated information into a personal knowledge base, and effectively apply information [50]. Pouliot et al. defined ML as the degree to which individuals can obtain, comprehend, communicate, calculate and process patient-specific information about their medications to make informed medication and health decisions [51]. The MIL emphasizes the comprehensive, critical, proactive and interactive capabilities of people in the process of acquiring medication information [52]. Pregnant women with adequate (or “high”) MIL are characterized with the ability to actively identify the need for medication information; efficiently obtain needed medication information; exactly comprehend required medication information; strictly evaluate medication information; and fully use medication information to make informed medication-related decisions [50, 51]. Moreover, in addition to general health outcomes, a few studies have found an association between inadequate HL and medication-related skills, including dosing errors [53], misunderstanding of prescription labels [54], poorer ability to take medication appropriately [55, 56], and lack of knowledge of dosing [57]. A lack of adequate MIL could result in poor medication adherence and the misunderstanding of medication-related information in pregnant women, which could in turn make pregnant women more prone to medication errors that adversely affect maternal and child health [58].

Pregnant women always have a higher risk perception of medication, which may negatively influence decisions in self-management, disease prevention, and health promotion, especially discourage them from taking medication as prescribed [59]. The behavior of medication usage is often related to the risk perception of medications [60]. Excessive risk perception may result in self-withdrawal of medication, and low risk perception may lead to self-medication. It is an indisputable fact that the compliance of medication during pregnancy is very low [10, 61]. What’s more, an observational cohort study by Toussaint et al. showed that self-medication was common before or during pregnancy, which might expose pregnant women to medication that may have negative effects on the fetus [1]. A study showed that respondents with a lower level of HL had more concerns about overuse and harmful effects of medication [62]. Therefore, extra attention should be given to pregnant women with inadequate MIL by healthcare professionals.

This cross-sectional survey showed that only 27.9% of pregnant women have adequate MIL, which was consistent with the results of Zhang et al [27]. Pregnant women have a higher need for medication information and are more cautious with medication use, but their MIL is generally at a low level and they lack the ability to discriminate between sources and quality of medication information. Weekes’ research showed that when using medications during pregnancy, the knowledge, information and tools required for health decisions were often lacking, which could result in pregnant women obtaining inconsistent or inaccurate medication information and needing adequate MIL to determine if a drug treatment poses a risk [63]. It is necessary and urgent to empower pregnant women to seek, comprehend, appraise and apply medication information. Similar to Vamos’study [17], the domain of medication information quality discrimination had the lowest score. Vamos et al. showed that pregnant women often feel anxious and fearful about online information, and they sought external assistance (from friends or healthcare providers) to assess the authenticity of the information [17]. These phenomena were in line with the low level of medication information quality discrimination in the current study.

The newly-developed MILS can serve as a screening tool to identify those pregnant women with inadequate MIL who may need additional assistance or intervention. A healthcare professional could help those women voice their concerns and offer relevant guidance to resolve these issues. Clinicians and nurses could use simpler terms, pictorial aids and/or visual medication schedules when educating or counselling these pregnant women to enhance their understanding of medication-related materials. In addition, the MILS could help healthcare professionals to recognize specific deficiencies areas where education about medications requires additional focus and improvement. The ultimate goal of medication education is to prevent potential errors or harm resulting from the misunderstanding of medication information in pregnant women.

Strengths and limitations

Previous studies have developed tools to measure medication-related HL, but they mainly related to specific medications or diseases such as an oral anticoagulation knowledge tool (OAK) and patient anticoagulation knowledge tool (PAK) [64, 65], Oral Anticoagulation Knowledge Tool (AKT) [66], the HIV Treatment Knowledge Scale (HIV-TKS) [67], with few studies involving pregnant women. Adequate MIL is important for pregnant women who require medication to help them accurately identify and take appropriate medication. The MILS is intended to be used in assessing the MIL of pregnant women to help healthcare professionals identify and educate pregnant women in an appropriate and timely manner.

Limitations in this study should be acknowledged. Pregnant women were recruited from hospitals in central China, which might not be representative of all pregnant women. Additional research in other countries or regions is needed for further verification, especially women living in lower socio-economic areas. A second limitation was the test-retest reliability finding showing that follow-up was relatively high, which may cause data bias. Lastly, the criterion-related validity of the MILS could not be conducted due to a lack of relevant scales. Scales in different languages are also expected in the future.

The MILS shows good reliability and validity in assessing MIL of pregnant women, which may guide further interventions. A large-scale investigation is needed to evaluate the MIL of pregnant women so as to guide future research studies.

HL Health Literacy

ML Medication Literacy

MIL Medical Information Literacy

MILS Medical Information Literacy Scale

CMIS Comprehensive Model of Information Seeking

EFA Exploratory factor analysis

CFA Confirmatory factor analysis

KMO Kaiser-Meyer-Olkin

χ2/df Chi-square Mean/Degree of Freedom.

GFI Goodness-of-fit Index.

AGFI Adjusted Goodness-of-fit Index.

SRMR Standardized Root Mean Square Residual.

IFI Incremental Fit Index.

RMSEA Root Mean Square Error of Approximation.

CFI Comparative Fit Index.

TLI Tucker Lewis Index.

Ethics approval and consent to participate

This study has been performed in accordance with the Declaration of Helsinki. All methods were carried out in accordance with relevant guidelines and regulations in the declaration. Ethical approval for this study was granted by the ethics committee of Wuhan University School of Nursing (2020YF0083). Participation was voluntary, and participants could withdraw from the study at any point in time. Before the survey, a written explanation about the study purpose was provided to each participant. Informed consent was obtained from all participants.

Consent for publication

Not applicable

Availability of data and materials

The datasets generated and/or analysed during the current study are not publicly available due to them containing information that could compromise research participant consent but are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Authors' contributions

Conceptualization, L.-S.Y. and G.-J.Y.; methodology, L.-S.Y., G.-J.Y. and Z.J.; investigation, L.-S.Y., N.-S.Y. and S.Q.; data curation, Z.J. and S.Q.; writing—original draft preparation, L.-S.Y. and G.-J.Y.; writing—review and editing, O.Y.-Y.Q., S.R. and W.L.; supervision, O.Y.-Y.Q., C.-H.J. and W.L.; All authors have read and agreed to the published version of the manuscript.

Acknowledgements

We would like to thank the librarians for their help in finding literature relevant to this topic. We are grateful to Zhongnan Hospital of Wuhan University, which allowed us access to a large patient population.

Rouamba T, Valea I, Bognini JD, Kpoda H, Mens PF, Gomes MF, et al. Safety profile of drug use during pregnancy at peripheral health centres in Burkina Faso: A prospective observational cohort study. Drugs - Real Wor Outc. 2018;5(3):193-206. doi:10.1007/s40801-018-0141-1
Scaffidi J, Mol BW, Keelan JA. The pregnant women as a drug orphan: a global survey of registered clinical trials of pharmacological interventions in pregnancy. BJOG-Int Jobstet Gy. 2017;124(1):132-140.
Honein MA, Gilboa SM, Broussard CS. The need for safer medication use in pregnancy. Expert Rev Clin Phar. 2013;6(5):453-5. doi:10.1586/17512433.2013.827401
Thorpe PG, Gilboa SM, Hernandez-Diaz S, Lind J, Cragan JD, Briggs G, et al. Medications in the first trimester of pregnancy: most common exposures and critical gaps in understanding fetal risk. Pharmacoepidemiol Drug Saf. 2013;22(9):1013-8. doi:10.1002/pds.3495
Baer RJ, Chambers CD, Ryckman KK, Oltman SP, Rand L, Jelliffe-Pawlowski LL. 314: Risk of preterm birth by subtype for mothers with drug use during pregnancy by subtype for mothers with drug use during pregnancy. Am J Obstet Gynecol. 2017;216(1):S192-S193. doi:10.1016/j.ajog.2016.11.572
Ke AB, Greupink R, Abduljalil K. Drug Dosing in Pregnant Women: Challenges and Opportunities in Using Physiologically Based Pharmacokinetic Modeling and Simulations. CPT: pharmacometrics Syst Pharmacol. 2018;7(2):103-110. doi:10.1002/psp4.12274
MU LC. Prenatal care: Patient education, health promotion, and safety of commonly used drugs. https://www.uptodate.com/contents/prenatal-care-patient-education-health-promotion-and-safety-of-commonly-used-drugs/print?search=pregnancy. Accessed 27 March 2019.
Lee S, Khare MM, Olson HR, Chen AMH, Law AV. The TEACH trial: Tailored education to assist label comprehension and health literacy. 10.1016/j.sapharm.2018.05.015. Res Social Adm Pharm. 2018;14(9SI):839-845.
Ceulemans M, Lupattelli A, Nordeng H, Odalovic M, Twiggy M, Foulon V. Women's Beliefs About Medicines and Adherence to Pharmacotherapy in Pregnancy: Opportunities for Community Pharmacists. Res Social Adm Pharm. 2019;25(5):469-482.
Lupattelli A, Spigset O, Nordeng H. Adherence to medication for chronic disorders during pregnancy: results from a multinational study. Int J Clin Pnarm-net. 2014;36(1):145-53. doi:10.1007/s11096-013-9864-y
Pijpers EL, Kreijkamp-Kaspers S, McGuire TM, Deckx L, Brodribb W, van Driel ML. Women's questions about medicines in pregnancy - An analysis of calls to an Australian national medicines call centre. Aust N Z J Obstet Gynaecol. Jun 2017;57(3):334-341. doi:10.1111/ajo.12531
Widnes SF, Schjott J. Risk perception regarding drug use in pregnancy. Am J Obstet Gynecol. 2017;216(4):375-378.
Arbuckle C, Tomaszewski D, Brown L, Schommer J, Morisky D, Parlett-Pelleriti C, et al. Exploring the relationship of digital information sources and medication adherence. Comput Bio Med. 2019;109:303-310. doi:10.1016/j.compbiomed.2019.04.023
Vamos CA, Merrell L, Detman L, Louis J, Daley E. Exploring Women's Experiences in Accessing, Understanding, Appraising, and Applying Health Information During Pregnancy. J Midwifery Wom Heal. 2019;64(4):472-480.
Te Poel F, Baumgartner SE, Hartmann T, Tanis M. The curious case of cyberchondria: A longitudinal study on the reciprocal relationship between health anxiety and online health information seeking. J Anxiety Disord. 2016;43:32-40. doi:10.1016/j.janxdis.2016.07.009
Kirk JK. Connecting pharmacy and literacy: The North Carolina Medication Information Literacy Project. Acta Crystallographica. 2000;43(4):386–393.
Barnes LAJ, Barclay L, McCaffery K, Aslani P. Complementary medicine products information-seeking by pregnant and breastfeeding women in Australia. Midwifery. 2019;77:60-70. doi:10.1016/j.midw.2019.06.011
Vamos CA, Merrell L, Detman L, Louis J, Daley E. Exploring Women's Experiences in Accessing, Understanding, Appraising, and Applying Health Information During Pregnancy. J Midwifery Wom Heal. 2019;64(4):472-480. doi:10.1111/jmwh.12965
Dorst MT, Anders SH, Chennupati S, Chen Q, Purcell Jackson G. Health Information Technologies in the Support Systems of Pregnant Women and Their Caregivers: Mixed-Methods Study. . J Med Internet Res. 2019;21(5):e10865. doi:10.2196/10865
Kotlar B, Gerson E, Petrillo S, Langer A, Tiemeier H. The impact of the COVID-19 pandemic on maternal and perinatal health: a scoping review. Reprod health. 2021;18(1):10. doi:10.1186/s12978-021-01070-6
Carolan-Olah M, Sayakhot P. A randomized controlled trial of a web-based education intervention for women with gestational diabetes mellitus. Midwifery. 2019;68:39-47. doi:10.1016/j.midw.2018.08.019
Diaz JA, Griffith RA, Ng JJ, Reinert SE, Friedmann PD, Moulton AW. Patients' use of the Internet for medical information. J Gen Intern Med 2010;17(3):180-185.
Keselman A, Arnott Smith C, Murcko AC, Kaufman DR. Evaluating the Quality of Health Information in a Changing Digital Ecosystem. J Med Internet Res. 2019;21(2):e11129. doi:10.2196/11129
Lindquist LA, Go L, Fleisher J, Jain N, Friesema E, Baker DW. Relationship of health literacy to intentional and unintentional non-adherence of hospital discharge medications. J Gen Intern Med. 2012;27(2):173-8. doi:10.1007/s11606-011-1886-3
Mosher HJ, Lund BC, Kripalani S, Kaboli PJ. Association of health literacy with medication knowledge, adherence, and adverse drug events among elderly veterans. J Health Commun. 2012;17 Suppl 3:241-51. doi:10.1080/10810730.2012.712611
Shieh C, Mays R, McDaniel A, Yu J. Health literacy and its association with the use of information sources and with barriers to information seeking in clinic-based pregnant women. Health Care Women In. 2009;30(11):971-88. doi:10.1080/07399330903052152
Zhang N, Wang L, Ouyang YQ, Redding S. Survey on medication information literacy and influencing factors among pregnant Chinese women. J Matern Fetal Neonatal Med. 22 2019:1-8. doi:10.1080/14767058.2019.1642869
Barnes LAJ, Barclay L, McCaffery K, Aslani P. Complementary medicine products used in pregnancy and lactation and an examination of the information sources accessed pertaining to maternal health literacy: a systematic review of qualitative studies. BMC Complem Altern M. 2018;18(229)
Mafruhah OR, Huang YM, Shiyanbola OO, Shen GL, Lin HW. Ideal instruments used to measure health literacy related to medication use: A systematic review. Res Social Adm Pharm. 2021;17(10):1663-1672. doi:10.1016/j.sapharm.2021.01.017
Johnson JD, Donohue WA, Atkin CK, Johnson S. A comprehensive model of information seeking: Tests focusing on a technical organization. Science Communication. 1995;16(3):274-303.
Johnson JD, Meischke H. A Comprehensive Model of Cancer-Related Information Seeking Applied to Magazines. Hum Commun Res. 2006;19(3):343-367. doi:10.1111/j.1468-2958.1993.tb00305.x
Ellis D. A behavioural approach to information retrieval system design. Journal of documentation. 1989;
Wilson TD. Models in information behaviour research. Journal of documentation. 1999;55(3):249-270.
Hämeen-Anttila K, Kokki E, Lupattelli A, Nordeng H, Jyrkkä J, Vainio K, et al. Factors associated with the need for information about medicines among pregnant women - a multinational internet-based survey. Res Social Adm Pharm. 2015;11(2):297-302. doi:10.1016/j.sapharm.2014.06.003
Zhu C, Zeng R, Zhang W, Evans R, He R. Pregnancy-Related Information Seeking and Sharing in the Social Media Era Among Expectant Mothers: Qualitative Study. J Med Internet Res. 2019;21(12):e13694. doi:10.2196/13694
Boateng GO, Neilands TB, Frongillo EA, Melgar-Quiñonez HR, Young SL. Best Practices for Developing and Validating Scales for Health, Social, and Behavioral Research: A Primer. Review. 2018;6(149)doi:10.3389/fpubh.2018.00149
Leech NL BK, Morgan GA. IBM SPSS for intermediate statistics: use and interpretation: Routledge. 2014;
Nunnally JC BI. The theory of measurement error. Psychom Theory. 1994;3:209–47.
D H. Confirmatory factor analysis: Oxford university press. USA. 2008;
Wheaton B, Muthen, B. , Alwin, D.F , Summers, G. Assessing reliability and stability in panel models. Sociol Methodol. 1977;8(1):84–136.
Diamantopoulos A, Siguaw, J.A. Introducing LISREL. Sage Publications, London. 2000;
Byrne BM. Structural equation modeling with LISREL, PRELIS and SIMPLIS: basic concepts. Applications and Programming Lawrence Erlbaum Associates, Mahwah, New Jersey. 1998;
Şencan H. Sosyal ve Davranışsal Ölçümlerde Güvenirlik ve Geçerlik (Reliability and Validity in Social and Behavioral Measurements). (Ankara: Seçkin Yayınları); PMCid: PMC2361864. 2005;
Field A. Discovering statistics using IBM SPSS statistics. 5th ed.New York: NY: Routledge; 2013.
Zwick WR, and Velicer, W. F. Comparison of five rules for determining the number of components to retain. Psychol Bull. 1986;99:432–442. doi:10.1207/ s15327906mbr1702_5
Nie XQ LY, Li L. Statistic analysis of 2012 Chinese residents health literacy monitoring. Chin J Health Educ. 2014;30:178–181.
Nie X, Li Y, Li L. Statistic analysis of 2012 Chinese residents health literacy monitoring. Chin J Health Educ. 2014;30(2):178-181.
Pett MA, Lackey NR, Sullivan JJ. Making sense of factor analysis: The use of factor analysis for instrument development in health care research. 5th ed. New York, NY: Routledge; 2003.
Bernard HR, Bernard HR. Social research methods: Qualitative and quantitative approaches. 5th ed. New York, NY: Routledge; 2013.
ACRL. Information literacy competency standards for higher education. . www.ala.org/acrl/standards/informationliteracycompetency. Accessed January 2000.
Pouliot A, Vaillancourt R, Stacey D, Suter P. Defining and identifying concepts of medication literacy: An international perspective. Res Social Adm Pharm. 2018;14(9):797-804. doi:10.1016/j.sapharm.2017.11.005
Zhong ZQ, Shi SJ, Duan YL, Shen ZY, Zheng F, Ding SQ, et al. The Development and Psychometric Assessment of Chinese Medication Literacy Scale for Hypertensive Patients (C-MLSHP). Front Pharmacol. 2020;11490. doi:10.3389/fphar.2020.00490
Yin HS, Mendelsohn AL, Wolf MS, Parker RM, Fierman A, van Schaick L, et al. Parents' medication administration errors: role of dosing instruments and health literacy. Arch Pediat Adol Med. 2010;164(2):181-6. doi:10.1001/archpediatrics.2009.269
Davis TC, Wolf MS, Bass III PF, Thompson JA, Tilson HH, Neuberger M, et al. Literacy and misunderstanding prescription drug labels. Ann Intern Med. 2006;145(12):887-894.
Davis TC, Wolf MS, Bass PF, Middlebrooks M, Kennen E, Baker DW, et al. Low literacy impairs comprehension of prescription drug warning labels. J Gen Intern Med. 2006;21(8):847-851.
Fang MC, Machtinger EL, Wang F, Schillinger D. Health literacy and anticoagulation-related outcomes among patients taking warfarin. J Gen Intern Med. 2006;21(8):841-846.
Yin HS, Dreyer BP, Foltin G, van Schaick L, Mendelsohn AL. Association of low caregiver health literacy with reported use of nonstandardized dosing instruments and lack of knowledge of weight-based dosing. Ambulatory Pediatrics. 2007;7(4):292-298.
Yeh YC, Lin HW, Chang EH, Huang YM, Chen YC, Wang CY, et al. Development and validation of a Chinese medication literacy measure. Health Expect. 2017;20(6):1296-1301.
Widnes SF, Schjøtt J. Risk perception regarding drug use in pregnancy. Am J Obstet Gynecol. 2017;216(4):375-378. doi:10.1016/j.ajog.2016.12.007
Aljoher AM, Alsaeed MA, AlKhlfan MA, Almethen AW, Almukhaitah MA, Zareen H, et al. Pregnant women risk perception of medications and natural products use during pregnancy in Alahsa, Saudi Arabia. The Egyptian Journal of Hospital Medicine. 2018;70(1):13-20.
Ceulemans M, Lupattelli A, Nordeng H, Odalovic M, Twigg M, Foulon V. Women's Beliefs About Medicines and Adherence to Pharmacotherapy in Pregnancy: Opportunities for Community Pharmacists. Curr Pharm Design. 2019;25(5):469-482. doi:10.2174/1381612825666190321110420
Visscher BBB, Steunenberg BB, Zwikker HH, Heerdink ERR, Rademakers JJ. The impact of health literacy on beliefs about medication in a Dutch medication-using population. Eur J Clin Pharmacol. 2021;77(8):1219-1224. doi:10.1007/s00228-021-03105-0
Weekes L. Access to medicines information for pregnant women. J Pharm Pract Res. 2015;45(1):5–6.
Zeolla MM, Brodeur MR, Dominelli A, Haines ST, Allie ND. Development and validation of an instrument to determine patient knowledge: the oral anticoagulation knowledge test. Ann Pharmacother. 2006;40(4):633-8. doi:10.1345/aph.1G562
Briggs AL, Jackson TR, Bruce S, Shapiro NL. The development and performance validation of a tool to assess patient anticoagulation knowledge. Res Social Adm Pharm. 2005;1(1):40-59. doi:10.1016/j.sapharm.2004.12.002
Obamiro KO, Chalmers L, Bereznicki LR. Development and Validation of an Oral Anticoagulation Knowledge Tool (AKT). PloS one. 2016;11(6):e0158071. doi:10.1371/journal.pone.0158071
Balfour L, Kowal J, Tasca GA, Cooper CL, Angel JB, Macpherson PA, et al. Development and psychometric validation of the HIV Treatment Knowledge Scale. AIDS care. 2007;19(9):1141-8. doi:10.1080/0954012070135224

No competing interests reported.

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Development and psychometric properties of a medication information literacy scale for Chinese pregnant women

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Abstract

Background

Methods

Results

Conclusion

Figures

Background

Methods

Study design

Theoretical framework

Instrument development

Stage 1: selection of appropriate indicators

Stage 2: Scale development

Stage 3: Pilot survey

Stage 4: Psychometric testing of the MILS

Results

Socio-demographic characteristics of participants

Descriptive statistics of psychometric properties

Exploratory factor analysis (EFA)

Confirmatory factor analysis (CFA)

Reliability

Sensitivity analysis

Medication information literacy score

Discussion

Strengths and limitations

Conclusion

Abbreviations

Declarations

References

Additional Declarations

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