A Rasch Analysis of the Person-Centred Community Care Inventory (PERCCI): Older People’s Experiences of Long-Term Community Support in England.

doi:10.21203/rs.3.rs-4619263/v1

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A Rasch Analysis of the Person-Centred Community Care Inventory (PERCCI): Older People’s Experiences of Long-Term Community Support in England.

https://doi.org/10.21203/rs.3.rs-4619263/v1

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Background

Person-centredness has become the byword for a modern, high quality care service. Yet measurement of person-centred quality is fraught with difficulty, and existing instruments tend to rely on classical psychometric methods with many untestable assumptions. An important new instrument, the Person-Centred Community Care Inventory, has yet to be subject to modern psychometric techniques.

Methods

The same 22 initial items used to form the Person-Centred Community Care Inventory were re-examined under a Rasch framework. A re-analysis of nearly 600 questionnaire returns from older people in England with community care needs was undertaken, with this large sample partitioned into a ‘evaluation’ and ‘validation’ sample. In addition, a test-retest inspection of 77 repeat administrations was undertaken.

Results

Rasch analysis confirmed a more parsimonious form of the scale, comprising two subscales which broadly met key diagnostic tests. Six items evaluated person-centredness in care worker interactions, and a second six items evaluated person-centredness in care plans. When combined into a single 12 item scale using ‘testlets’, the resulting PERCCI-12 suggested satisfactory measurement properties overall, albeit with reduced internal reliability over two separate subscales. Test-retest reliability of the PERCCI-12 was estimated at 0.871 and was robust to sensitivity tests.

Conclusion

The Rasch evaluation of the PERCCI supports a shorter scale formed of 12 items. This can be analysed as two separate subscales, or as a single summary scale with weaker internal reliability. Future research is needed to reinforce construct validity, to improve interpretability, and to establish minimally important difference.

Patient-Reported Experience Measure

Rasch

Psychometrics

Person-Centred Care

Person-centredness has become the byword for quality across health and long-term care systems worldwide (WHO, 2007, 2015). Whilst literature on person-centred care is diverse and confounded by the use of varied prefixes to ‘centredness’ (e.g. person, patient, client, people, family – see (Naldemirci et al., 2018), there is central agreement about its foundational nature. All variants share a common humanist philosophy, tracing a common ancestral root to Rogerian psychology (Rogers, 1949), and taking a holistic view of the person, beyond any presenting concern or ‘need’ (McCormack & McCance, 2006). Each also shares concern about positivist, medicalised and impersonal models of long-term care, described as “too uniform; too analytical; too theoretical; too negative; and too directive” (Leplege et al., 2007). The concept further challenges the assumption that disease, disability and frailty are simply coterminous with ‘illness’, highlighting the unpredictable relationships between levels of functioning and cognitive impairment with life satisfaction (Albrecht & Devlieger, 1999; Wilberforce et al., 2017).

Despite being a defining attribute of many care quality frameworks (NICE, 2012), person-centred care is fraught with conceptual and practical challenges (Dewing, 2004; Leplege et al., 2007; McCormack et al., 2010; Naldemirci et al., 2018). For some, it is equated more generally with ‘just giving people what they want’ (Epstein & Street, 2011). Elsewhere, the concept has been selectively interpreted as being about executing personal choices and self-determination in care, and used to describe systems and processes that endow greater autonomy and independence to the end-user (Nolan et al., 2004). More generally, an academic tradition has spawned from multiple competing frameworks seeking to define person-centredness, with “a great deal of ink” being spilt in a “crowded conceptual space” (Mitchell, Cribb & Entwhistle 2022; p3)

Conceptual confusion can be observed in the policy arena, particularly in the home and community support of older people in England. Notions of person-centred care have become intertwined with national policies inspired by the Independent Living Movement and the activist voices of younger disabled adults seeking greater control over the resources and arrangements of community social care. For example, using the language of person-centredness, older people living at home needing care services, as well as working-age disabled people, may receive state-funded care budgets (directly in their bank account, or held on their behalf) to control and manage as an individual ‘commissioner’ of support (Social Care Institute for Excellence, 2012). Although perhaps giving direct action to the idea of ‘control’, these arrangements have been found to be detrimental to the wellbeing of older people (Netten et al., 2012; Woolham et al., 2016; Woolham & Benton, 2013), suggesting that different interpretations of person-centredness have tangible consequences for client outcomes.

For older people receiving support in the community, Kitwoodian models of person-centred care are perhaps more useful, in that they give primacy to preserving and promoting personhood in the context of social relationships and the experience of dementia (Kitwood, 1997). Such approaches place a value on supporting care relationships, and seek to attain a deep understanding of a person’s perspectives and interpretations during care encounters (Brooker, 2003). Although such models are widespread (Røsvik et al., 2011), they were developed just in relation to dementia care, predominantly for those with advanced stages of cognitive impairment and resident in care homes and institutional settings. For older people living in the community, how applicable this approach would be in promoting care quality is unclear.

For researchers, policy-makers and practitioners seeking to understand quality in community care, these uncertainties cause real dilemmas. This is particularly the case with respect to how the quality of person-centred care can be measured (National Voices, 2018). There are significant doubts that instruments designed to evaluate person-centredness in working-age services will adequately capture the themes of importance to older people. Yet measures designed for older people’s services tend to be specific to only dementia, and often based on resource-intensive observations (Edvardsson & Innes, 2010). There is also evidence of a focus towards institutional settings, with questions relating to the quality of the care environment, unsuited to domiciliary support. Indeed, a systematic review of measures designed for self-completion found just one used in community settings, which itself had not satisfied psychometric tests (Wilberforce et al., 2016). Against this background a new measurement instrument was designed to evaluation person-centredness for older people with care needs in community-based settings.

Development of the Person-Centred Community Care Inventory (PERCCI)

The PERCCI is a self-administered questionnaire designed through a participatory process involving older service users living in the community and receiving a blend of mental health and social care services (Wilberforce et al., 2018). It was developed through a three stage process. First, a literature review-based concept synthesis(Wilberforce et al., 2017) thematised expositions of person-centred care as it applied to community social care. Twelve attributes of person-centredness were organised within three overarching themes: (i) understanding the person; (ii) engagement in decision-making; and (iii) promoting care relationships. Second, concept mapping methodology(Rosas & Ridings, 2016) engaged 22 older people using community services to identify 67 statements which people used to describe good (and bad) care experiences (Wilberforce et al., 2016). These statements were mapped to the 3 themes of person-centredness (or else classified as outside the scope of the study where they did not correspond). Third, the statements were reformulated as questionnaire items, and cognitively tested through qualitative methods, with 22 central items spanning the conceptual framework being administered with an achieved postal sample of nearly 600 older people using long-term services (outlined below).

To date, only ‘classical’ psychometric analysis has been undertaken, detailed in Wilberforce et al., (2018). Factor analysis identified that items could be best organised in two scales: those questions relating to person-centredness exhibited by care workers; and person-centredness exhibited by the organisation of care. Using a bifactor model, it was established that a single unidimensional structure could be justified on the basis of sufficient shared variance between the two primary factors. The final scale, reduced to 18 items, had excellent test-retest reliability, and hypothesis tests formed a priori were supported by the instrument, indicating encouraging evidence of validity.

However, there are well-established concerns with classical psychometric testing (Pallant & Tennant 2007). Scales drawn from a simple ‘raw scores’ using short-ordinal response formats are assumed to have properties of interval-level data, but this demands very strong assumptions. For example, it requires that every item in the instrument, and every interval in the Likert-type response categories, each conveys equal and identical information. A further assumption is that all items are parallel, independent, measures; that is, the scores provided by any two items should not be related to each other except through the phenomenon being measured. Yet, in practice, the pursuit of high internal reliability statistics in classical testing encourages ‘local dependency’, resulting in artificially-inflated estimates of precision (Ip, 2001). A further concern is the suitability of analytical procedures using distribution-based statistics when assumptions are not met. Even calculations of simple means and associated hypothesis tests could be compromised. But perhaps the strongest critique of classical testing is that it proves perilously difficult to empirically evaluate these strong assumptions (Hobart et al., 2007).

Rasch analysis provides an alternative framework with the primary goal of ensuring that assertions about interval-level measurement are valid (Rasch, 1960). Developed in education sciences, the approach is based on identifying hierarchical patterns across items akin to a probabilistic form of a Guttman scale. That is, the likelihood of a respondent affirming any particular item is linked to the level or strength of the attribute the item represents, with items being ranked and positioned accordingly on the continuum of measurement. Under testable assumptions, researchers and practitioners using scales that satisfy Rasch criteria can be confident that measurement is at the interval-level (Pallant & Tennant 2007). Furthermore, the Rasch paradigm’s careful attention to the performance of individual items, and diagnostic indicators that are well-suited to identifying rectifiable problems with individual items, the Rasch paradigm is well-suited to formative evaluation early in the development of new instruments. A further strength is in reducing scales to an efficient form, by highlighting how each item performs and adds information at different places along the latent continuum.

Consequently, this study aimed to form a Rasch version of the Person-Centred Community Care Inventory (PERCCI) and report its psychometric properties.

This paper uses the same dataset as that reported in Wilberforce et al (2018) but uses a Rasch-informed approach to evaluating the questionnaire items. Participants were all older people (aged 65+) with mental health and social care needs living at home, and who had been referred to multidisciplinary mental health services within the catchment of five English NHS Trusts. People without capacity to consent, those in hospital and those with moderate to severe dementia were excluded. All eligible people on service caseloads were invited to complete a postal questionnaire through a central mailing, and a reminder sent to non-respondents after 2 weeks. The questionnaire sought agreement/disagreement on 22 positive-keyed statements, using a four-point Likert scale. Additionally, respondents were asked a single ‘Friends and Family Test’ question, used across the English NHS as a generic instrument aiming to evaluate care experiences (elsewhere known as the ‘Net Promoter Score’). Fieldwork closed in May 2016 with 596 returns (response rate of 29%). Sample characteristics are already reported elsewhere (Wilberforce et al 2018). In brief, 62 percent of survey responses were from women, 25 percent were from people aged 80+, and using matched data from local services it is known that approximately one-third had dementia and 28 percent received homecare. Forty-two percent of respondents reported receiving support in completing the questionnaire from family or friends.

Data were analysed using a partial credit model(Andrich, 1978) estimated within RUMM2030 software. Seven elements of fit to the Rasch model are evaluated(Pallant & Tennant, 2007) shown in Table 1.

[Table 1]

Sample size recommendations for Rasch analysis are not definitively established but are typically in the range of 150–250 (Linacre 1994). Very large samples can lead to goodness-of-fit tests being over-powered; that is, even acceptable levels of deviation from the Rasch model are found to be statistically significant. Taking advantage of a large sample size (n > 500 – after Gibbons et al 2013), the sample was randomly divided into two groups, with Rasch analysis performed on the first sample to identify an optimal scale (‘evaluation sample’), with fit confirmed in the second group as a validation check (‘validation sample’). As with the bifactor analysis, RUMM2030 enables an inspection of whether two scales can be suitably combined and treated as unidimensional using ‘testlets’.

Two final analyses were performed. First, a simple correlation between the PERCCI and Friends and Family Test sought to assess the PERCCI against an a priori prediction that a strong positive correlation would be found. Second, test-retest reliability was enabled by posting a second questionnaire to arrive 2–3 weeks after the first was returned, with 77 replies (59% response rate). Analysis used a two-way random effects intra-class correlation coefficient. Three sets of sensitivity analyses for the test-retest were conducted: (i) the sample was restricted to those with a retest period of at most one month after the initial administration; (ii) the sample was restricted to those with at most a one point change in the Friends and Family Test between the test and retest period; (iii) mean imputation was used to include retest scores only partially completed.

Scale 1: Person-Centred Care Workers

Thirteen items pertaining to person-centred care as exhibited by care workers were evaluated against the Rasch criteria (Table 2, line A). The items had a reasonable fit according to the χ² statistic, and residual standard deviations were not far distant from expectations. There was no DIF present with respect to any of the four characteristics examined (age, gender, whether the respondent received homecare, and whether the person received help in completing the questionnaire). All items had well-ordered category characteristic curves.

However, there was evidence of local dependency between items 6 (‘Care workers can tell when I am feeling down’) and 7 (‘care workers can tell my good days from my bad days’), with highly correlated residuals (r = 0.303). Relatedly, the 13 items appeared to indicate redundancy with a very high Cronbach alpha (= 0.942). Other investigations illustrated that the 13 items were poorly-targeted, with a mean person location of 2.28. Item thresholds across the continuum of measurement are presented in Fig. 1 (ordered by mean item difficulty).

[Figure 1 here]

To improve the efficiency of the measure, and resolve local dependency, items were removed. Judgements on which items to remove were taken based largely on the concerns over mis-targeting, with the intent that any removed items would be those that proved relatively easy to endorse (whilst ensuring that the key domains of person-centred care were still represented in the remaining items). For example, item 15 (‘my care workers speak to me in a friendly and respectful manner’) had a mean location of -1.09 (thus very easy to endorse even in poorer quality services), whilst several other items represented aspects of person-centred communication, meaning it could be safely removed without upsetting construct validity.

A final set of six items had acceptable fit statistics overall, represented a unidimensional construct and was free from dependence. However, more than a quarter of respondents were located at the ceiling or floor (overwhelmingly the former) indicating that many participants did not find they could discriminate between care workers using the questions. A repeat analysis in the validation sample confirmed the findings.

Scale 2: Person-Centred Organisation of Care

An initial assessment of the nine items concluded that, collectively, they were poorly fitting the Rasch assumptions. Item level analysis identified two items with F-statistics indicating substantial misfit (Item 22: fit residual = 3.23, F(4, 289) = 5.09, p < 0.001; and Item 26: fit residual=-4.04, F(4, 287) = 5.92, p < 0.001;). Furthermore, there were breaches of unidimensionality and local dependence; residuals for items 25 and 26 were significantly correlated beyond the average (r = 0.391). Items 22 and 26 also displayed evidence of (uniform) DIF with respect to both age and gender. For example, item 22 (‘care and support is provided at times that suit me’) was more commonly affirmed by women then men at each point of the measurement continuum (see Fig. 2). As with the first scale (above), there was also evidence of mistargeting. Relative to the questionnaire items, respondents were distributed with a mean significantly greater than zero, suggesting that items were too ‘easy’ to affirm on average. However, there was no problem with ceiling/floor effects, signalling that measurement was still feasible across the continuum.

[Figure 2 here]

Items 22 and 26 were removed from the scale, improving the fit of the scale to the Rasch assumptions. Reviewing seven remaining items, a pragmatic decision was taken to remove item 28 (“I am given opportunity to join groups where I can meet other people”) since it had a relatively high number of missing values (> 6%) and recalling some concerns in earlier cognitive testing that divided opinion over whether meeting other people was always to be encouraged in person-centred care. A final scale of six items broadly satisfied Rasch criteria, with a non-significant χ² statistic, albeit with a larger-than-ideal standard deviation for items and persons. This general conclusion was confirmed in the validation sample.

Table 2 presents item-level diagnostics for the final 12 items in the Rasch analysis. This highlights a stability in the item locations between evaluation and validation samples, with all items remaining in the same rank ordering, save for items 11 & 7 (with similar mean difficulty) which were reversed.

Summary scale

A further analysis tested the potential for a summary scale formed of the final items. Predictably, the 12 items could not be entered as a single unidimensional scale, as evidenced by the diagnostics (Table 2, row G). Echoing the classical factor analysis in Wilberforce et al (2018), evidence of multidimensional structure is evidenced. However, using testlets within RUMM2030, items within each subscale were collapsed into a ‘meta-item’, resolving the inherent dependency between them, but inevitably at the expense of internal reliability (or ‘separability’). The person-item threshold distribution is shown in Fig. 3. There remained some mis-targeting, with a mean respondent location of 0.822 logits. However, item thresholds were well spread between − 2 and + 2 logits.

The correlation between the PERCCI and Friends and Family Test was 0.630, suggesting high association.

[Figure 3]

Final PERCCI-12

The 12 items forming the PERCCI are shown in Table 4. To enable a conversion from raw scores to interval-level scores, a nomogram is presented as Table 5.

[Table 4]

[Table 5]

Test-retest reliability

Test-retest analysis of the raw scores of the 12-item PERCCI scale was possible for 77 participants who returned completed questionnaires at two time points. The two-way random effects ICC estimate was 0.871 [0.805, 0.916]. The Limits of Agreement are illustrated through the Bland Altman plot (Fig. 4). The standard error of measurement is calculated as \(\sigma \sqrt{(1-ICC)}\) =3.215, spanning 8 percent of the full breadth of the scale. Three sets of sensitivity analyses for the test-retest were conducted as outlined above. These tests resulted in central ICC estimates of 0.873, 0.864 and 0.831 respectively.

[Figure 4]

Measurement problems have been identified as a leading contributor to inconclusive or low quality evidence for person-centred care(Edvardsson & Innes, 2010; Kogan et al., 2016; Rathert et al., 2013; Scholl et al., 2014; Zill et al., 2013) and in a review of expert opinion, poor quality measurement has been identified as a priority theme for action (Harding et al 2015). These calls are not contained to academia, with service user groups adding their voices to calls for more attention to measurement of person-centred qualities (National Voices, 2018). The PERCCI is one step in this direction.

It is instructive to position the PERCCI within quality measurement frameworks. The health sciences landscape is awash with Patient-Reported Outcome Measurement (PROMs), which confusingly often claim to be ‘person-centred measures’, on the basis that they draw on direct patient reports (rather than clinical ratings of health outcomes). Yet, any judgement of service quality cannot rely on outcomes alone, and Donabedian frameworks also highlight crucial process features of care systems (Donabedian, 2005). In the context of old age, where many health conditions (including dementia) are progressive and irreversible, experiential features of care processes and delivery become particularly important. Thus, growing attention is paid to ‘Patient-Reported Experience Measures’ (PREMs) as a crucial complement to PROMS in service evaluation (Stanhope et al 2021).

The previously published ‘classical’ version of the PERCCI (Wilberforce et al 2018) ostensibly had strong measurement properties, which may beg the question of why this Rasch version is necessary. The weaknesses of classical versions are well-documented elsewhere (Pallant & Tennant, 2007), but chief among them is that too many of their assumptions are unfalsifiable, which breaches foundational principles of scientific study. Reliance of measures such as Cronbach alpha for internal reliability has given rise to many questionable design characteristics, such as generating item statements with very high local dependency (in effect offering an artificial boost to its precision). Classical versions of many instruments also struggle to verify that they satisfy the need for interval-level measurement, or whether (instead) they should be considered as generating ordinal-level data. Scales can also become bloated with excessive items, whereas modern psychometric methods with item-level diagnostics enable superior efficiency (Kazemi & Kajonius, 2021). In the case of the PERCCI, the 12-item scale has reasonable measurement properties against Rasch assumptions. The even balance between items relating to care workers (the first six questions) and organisation-level factors (the latter six) gives good ‘face validity’ and the content relates well to the overall framework of person-centredness. However, the act of combining the two separate factors does have the consequence of reducing internal consistency to a more modest level.

The 12-item PERCCI has been made freely available (www.percci.org), and is already being adopted within research and practice. Its items were rooted in co-production processes and centred on the voices of lived experience (Wilberforce et al 2018), and hence has high acceptability. Such acceptability is highlighted by the PERCCI-12’s swift uptake, including local evaluations of service quality in Wales and Sweden. The PERCCI-12 has already been adopted within clinical trials as a key outcome measure and has been recommended by wider research groups (Orellana et al 2021; Tiffin & Roberts, 2024). A particular advantage of the PERCCI over other instruments evaluating person-centredness is that it can be summarised within a single numerical score. For example, recent instruments include the Person-centred Practice Inventory – Care (PCPI-C) which, despite a strong theoretical foundation, has 18 items divided across five separate factors (McCormack et al 2024).

However, the evidence-base for the PERCCI-12 can only be regarded as ‘emerging’, and key limitations must be borne in mind. The analysis presented here provides no validation against external ‘gold-standard’ metrics of person-centredness. Indeed, a central criticism of all instruments assessing person-centred care is that no such standard exists, and validation tends to rely on reviewing item content alone. Given the constant conceptual challenges facing researchers of person-centredness (Mitchell, Cribb & Entwistle, 2022), this remains a substantial weakness of the field. Nevertheless, further work is underway to establish expected correlations with Quality of Life instruments in dementia care, which may at least offer some concurrent validity inspection. A second concern is that the interpretability of the PERCCI-12 remains opaque. What does a ‘good’ score look like? What would suggest that improvement is required? Again, in common with other measures of person-centred care, the PERCCI has no benchmarks or reference points against which to interpret scores. An important future piece of work would be to establish a minimally important difference (MID) for the PERCCI, so that service evaluators and researchers can establish whether any variations in scores have real, practical implications for services. A final critique of a Rasch analysis is that raw data collected must be rescaled before they can be used for interval-level measurement. Although a simple arithmetic conversion, this adds a layer of complexity that may inhibit its use in practice.

The PERCCI-12 offers a brief survey to evaluate care quality in community services for older people. Analysis under modern psychometric techniques suggest that the PERCCI-12 broadly satisfies the Rasch assumptions and can be used for interval-level measurement. Additionally, the PERCCI-12 has good test-retest reliability. As with other measures of person-centred care, further work is needed to confirm construct validity and to examine minimally important difference in the scale.

Ethics approval and consent to participate

The analysis presented constitutes a re-analysis of existing data, stored anonymously. Ethics permissions governing the original data collection exercise is published elsewhere.

Consent for publication

Not applicable

Availability of data and materials

The PERCCI instrument is available at www.percci.org. The datasets used and/or analysed during the current analysis are available from the corresponding author on reasonable request.

Competing interests

The author developed the PERCCI instrument presented in this article as part of his doctoral studies.

Funding

No funding contributed to this reanalysis. Funding details for the original study is published elsewhere.

Authors' contributions

The sole author is responsible in full for all elements of the manuscript.

Acknowledgements

The author acknowledges the doctoral study supervision team that guided the original data collection: Professors David Challis, Linda Davies, Mike Kelly and Chris Roberts.

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Tables 1 to 5 are available in the Supplementary Files section.

No competing interests reported.

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A Rasch Analysis of the Person-Centred Community Care Inventory (PERCCI): Older People’s Experiences of Long-Term Community Support in England.

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Abstract

Figures

Background

Development of the Person-Centred Community Care Inventory (PERCCI)

Methods

Results

Scale 1: Person-Centred Care Workers

Scale 2: Person-Centred Organisation of Care

Summary scale

Final PERCCI-12

Test-retest reliability

Discussion

Conclusions

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1