Burned-out but proud Portuguese family doctors
Bibliographic record
Abstract
Background: Burnout syndrome (BS) is increasing among health professionals, including family doctors (FD). Aim: To characterize the prevalence of BS in a sample of FDs working in the Portuguese National Health System. Design: Cross-sectional survey. Setting: Primary Health Care Centers (HCC) from the 18 continental districts and 2 archipelagos of Portugal. Method: The Portuguese version of the Maslach Burnout Inventory - Human Services Survey (MBI - HSS) was sent to 40 randomly selected health-care centers (HCC) and distributed to the FDs employed. Sociodemographic and work-related data was also collected. Participants were classified as having high, average or low levels of emotional exhaustion (EE), depersonalization (DP) and personal accomplishment (PA) dimensions of burnout. Results: 371 questionnaires were sent, of which 153 (83 women, age range 29-64 years; response rate 41%) returned. One quarter (25.5%) of participants had high EE, 10.1% high DP and 11.4% high PA, but only 2.0% of participants scored high for all three dimensions. Women had significantly higher DP and PA scores than men; increased daily workload also led to increased PA scores. Conversely, no association was found between BS scores and age, marital status, number of years of practice or type of HCC (Family or Personalized). Conclusion: High burnout is relatively common among Portuguese family doctors, yet slightly lower than reported for other European countries. Burnout relief measures should be developed in order to prevent a further increase of BS among Portuguese FDs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".