Prevalence and Characteristics of Burnout among Pharmacists in Primary Care Centers in the Kingdom of Bahrain- A Cross-Sectional Study
Bibliographic record
Abstract
INTRODUCTION: Burnout syndrome is defined as the state of physical, emotional, and mental exhaustion that results from exposure to stressors. It is prevalent among healthcare workers including pharmacists and is associated with significant detrimental effects on the patients, healthcare workers, and healthcare systems. Nonetheless, few studies have assessed the prevalence and characteristics of burnout among pharmacists. This study aimed to assess the prevalence and characteristics of burnout among pharmacists in governmental primary health care centers in Bahrain. METHODS: A cross-sectional study was conducted in the period between January 2022 and February 2022 and involved all the pharmacists in the primary health care centers in the kingdom of Bahrain. Burnout syndrome was assessed using the Maslach Burnout Inventory, a validated tool designed to assess the emotional exhaustion, depersonalization, and personal accomplishment aspects of burnout. RESULTS: A total of 148 pharmacists completed the online questionnaire and were included in the analysis (response rate = 80.4%). The majority of participants were females (n = 130, 87.8%), married (n = 117, 79.1%), and aged between 25 and 35 years (n = 99, 66.9%). Almost 60% (n = 86, 58%) of the pharmacists had high levels of emotional exhaustion, 62 (41.9%) participants reported high levels of depersonalization, and 60.1% (n = 89) of them reported low accomplishment levels. No statistical differences were found between the baseline characteristics of the pharmacists and the aspects of burnout. CONCLUSION: In conclusion, this study revealed an alarmingly high prevalence of burnout syndrome among pharmacists in primary care centers in Bahrain. Evidence-based preventive strategies and interventions to reduce burnout levels among pharmacists are urgently needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".