Physician burnout and patient care practices in Sierra Leone
Bibliographic record
Abstract
Burnout, characterized by emotional exhaustion, depersonalization, and reduced personal accomplishment, can negatively impact patient care. While well-studied in developed countries, burnout in resource-limited settings like Sierra Leone is under-researched. We aimed to determine the prevalence of burnout among public sector physicians in Sierra Leone and assess its association with self-reported patient care practices. We conducted a cross-sectional study of 139 physicians randomly selected from major government hospitals across Sierra Leone. Burnout was assessed using the Maslach Burnout Inventory, and self-reported patient care practices were evaluated using an adapted version of Shanafelt et al.'s 2002 questionnaire. Statistical analyses included chi-square and fisher's exact test to compare demographic variables, and patient care practices between burnout and non-burnout physicians. Of 139 physicians surveyed (30% female), 23.7% met criteria for burnout, defined by two of the three criteria: high emotional exhaustion, high depersonalization scores and low personal accomplishment. Most respondents were aged 26–35 years. Burnout prevalence did not significantly differ by gender, age group, marital status, region, or practice level. Physicians with burnout reported high emotional exhaustion (32%), depersonalization (22%), and low personal accomplishment (39%). Physicians with burnout showed suboptimal patient care practices, including avoiding necessary diagnostic tests due to cost concerns, expediting discharges, and prescribing without adequate patient evaluation. Physician burnout is prevalent in Sierra Leone and is correlated with compromised patient care practices. Addressing burnout necessitates multifaceted interventions at individual, organizational, and systemic levels. Implementing support systems and promoting well-being among physicians may improve patient care outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".