Prevalence and factors associated with burnout among frontline primary health care providers in low- and middle-income countries: A systematic review
Bibliographic record
Abstract
<ns4:p> <ns4:bold>Background:</ns4:bold> Primary health care (PHC) systems require motivated and well-trained frontline providers, but are increasingly challenged by the growing global shortage of health care workers. Burnout, defined as emotional exhaustion, depersonalization, and low personal achievement, negatively impacts motivation and may further decrease productivity of already limited workforces. The objective of this review was to analyze the prevalence of and factors associated with provider burnout in low and middle-income countries (LMICs). </ns4:p> <ns4:p> <ns4:bold>Methods:</ns4:bold> We performed a systematic review of articles on outpatient provider burnout in LMICs published up to 2016 in three electronic databases (EMBASE, MEDLINE, and CAB). Articles were reviewed to identify prevalence of factors associated with provider burnout. </ns4:p> <ns4:p> <ns4:bold>Results:</ns4:bold> A total of 6,182 articles were identified, with 20 meeting eligibility criteria. We found heterogeneity in definition and prevalence of burnout. Most studies assessed burnout using the Maslach Burnout Inventory. All three dimensions of burnout were seen across multiple cadres (physicians, nurses, community health workers, midwives, and pharmacists). Frontline nurses in South Africa had the highest prevalence of high emotional exhaustion and depersonalization, while PHC providers in Lebanon had the highest reported prevalence of low personal achievement. Higher provider burnout (for example, among nurses, pharmacists, and rural health workers) was associated with high job stress, high time pressure and workload, and lack of organizational support. </ns4:p> <ns4:p> <ns4:bold>Conclusions:</ns4:bold> Our comprehensive review of published literature showed that provider burnout is prevalent across various health care providers in LMICs. Further studies are required to better measure the causes and consequences of burnout and guide the development of effective interventions to reduce or prevent burnout. </ns4:p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".