Duración de las licencias médicas FONASA por trastornos mentales y del comportamiento
Bibliographic record
Abstract
BACKGROUND: In Chile, the number of sick leaves due to mental health problems has systematically increased in recent years. AIM: To perform an analysis of sick leaves due to mental problems managed by the Fondo Nacional de Salud (FONASA) during 2008. MATERIAL AND METHODS: Analysis of all sick leaves awarded during 2008 for mental or behavioral problems, that were managed at FONASA. A negative binomial regression, was performed to predict the effects of different variables on the total duration of sick leaves. RESULTS: A total of 546,477 sick leaves were awarded to 198,752 individuals (2.27 per subject). The mean duration of each leave was 15.6 days. Summing all leaves, the lapse off work was 98 ± 96 days (median 65 days). Women had longer leaves than men. The type of medical leave, occupation, working for private or public institutions, economic activity and diagnosis were significantly associated with duration of time off work. CONCLUSIONS: Sick leaves for mental problems are prolonged and related to gender and socioeconomic variables.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".