No physician gender difference in prescription of sick-leave certification: A retrospective study of the Skaraborg Primary Care Database
Bibliographic record
Abstract
OBJECTIVE: The primary objective was to investigate how physicians' gender and level of experience affects the rate and length of sick-leave certificate prescription. The secondary objective was to study the physicians' gender and professional experience in relation to the diagnoses on the certificates. DESIGN: Retrospective, cross-sectional study of computerized medical records from 24 health care centres in 2005. SETTING: Primary care in Sweden. SUBJECTS: Primary care physicians (n = 589) and patients (n = 88 780) aged 18-64 years. MAIN OUTCOME MEASURES: Rate and duration of sick leave certified by different categories of physicians and for different diagnoses and gender of patients. RESULTS: Sick leave was certified in 9.0% (musculoskeletal (3%) and psychiatric (2.3%) diagnoses were most common) of all contacts and the mean duration was 32.2 days. Overall there was no difference between male and female physicians in the sick-leave certification prescription rate (9.1% vs. 9.0%) or duration of sick leave (32.1 vs. 32.6 days). The duration of sick leave was associated with the physician's level of professional experience in general practice (GPs (Distriktläkare) 37, GP trainees (ST-läkare) 26, interns (AT-läkare) 20 and locum (vikarier) 19 days, p < 0.001). CONCLUSION: Contrary to earlier studies we found no difference in sick-leave certification prescription rate and length between male and female physicians.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".