Study design:
According to the purpose of our study, a prospective cross-sectional survey was conducted.
Inclusion criteria and study settings:
The study concerned consenting medical practitioners, regardless of age or gender. These included medical doctors and doctoral student in medicine working in clinical practice in Gabon at the time of the study. Gabon is a sub-Saharan African middle-income country. It is populated by about 1.8 million inhabitants with about 4 physicians per 10,000 inhabitants (6), but these physicians are unequally distributed on the national territory (7).
Sample size:
We determined the minimum sample size required for our survey based on an acceptable margin error of 5%, a confidence level of 95% and an expected prevalence of severe burnout of approximately 7%, average prevalence reported in the literature (8). Thus, by applying the formula n = z² x p (1 - p) / m² (9) (z: for 95% = 1.96, p: assumed prevalence = 7% and m: acceptable margin error = 5%), a minimum of 100 practitioners were required. The source population was physicians and doctoral students in medicine working in clinical practice in Gabon and registered on the Facebook forum of physicians in Gabon, and the WhatsApp forum of medical students in Gabon. These online forums bring together the majority of medical practitioners in Gabon.
Sampling and data collection:
Each anonymous questionnaire completed and submitted online was systematically collected on the database via google form. The selection was simillary to a simple random draw. Data collection took place from November 1, 2018 to April 1, 2019.
Variables operationalization:
- Practitioners feelings about burnout were collected through the Maslach Burnout Inventory Scale (10). Therefore, the following outcomes criteria have been defined:
• severe burnout defined by high level score in all 3 dimensions of the Maslach Burnout Inventory Scale: emotional exhaustion (EE) ≥27 and depersonalization (DP) ≥10 and personal achievement (PA) ≤33. Severe burnout was coded as a binary categorical variable (yes = 1, no = 0).
• burnout (or burnout symptom) defined by the existence of at least one high level score in one of the 3 dimensions of the Maslach Burnout Inventory Scale: EE score ≥27 or DP score ≥10 or AP score ≤33. The burnout (the burnout symptom) was coded in binary categorical variable (yes = 1, no = 0).
- psychometric data related to the feelings felt by practitioners in relation to: ease of access to the hospital, the weekly frequency of the professional medical activity outside the public hospital, the frequency of activity in non-professional associations and opinion favorable to traditional medicine were collected by self-evaluation through questions on 7-level Likert scale or analog scale from 0 to 7. The feeling for each item was transcribed into a score and treated as a numerical variable.
- demographic and socioprofessional data: gender, marital status, hospital status (doctor versus doctoral medical student), hospital facility attended and means of transport from hospital to home were coded as dichotomous categorical variables; then, the age, the number of dependent children (living with the practitioner), the number of elderly dependents (living with the practitioner), the estimated average number of patients taken care of per day were coded as numerical variables.
Statistical analysis
Descriptive study:
Categorical variables were summarized as a percentage expressed with a 95% confidence interval (95% CI). The numerical variables were summarized by the median expressed with an interquartile range (IQR).
Analytical study:
- univariate analysis to evaluate the link between one variable and burnout outcome was performed by Fisher’s Chi-square test for categorical variables and the Mann-Whitney U-test for numerical variables. Variables whose relationship with burnout had a statistical significance of less than 25% (p <0.25) were selected for the multivariate analysis. Age, sex and place of residence relative to hospital (ie same or different borough from that of the hospital) were considered as forced variables for multivariate analysis
- multivariate analysis made by the binary multiple logistic regression using the backward method. According to the adjusted odd ratio (aOR), estimator of the association between the burnout symptom event and one factor independently of others, logistic regression generate a predictor model of burnout symptoms prevalent (at the statistical significance threshold p <0.05). The quality of the predictor model was evaluated by the Nagelkerke R-two and the Hosmer-Lemeshow test. Software SPSS 21 (Statistical Package for the Social Sciences) version in French was used.