Prestación por riesgo durante el embarazo e incapacidad temporal en una cohorte de trabajadoras del Parc de Salut Mar (Barcelona, España)
Bibliographic record
Abstract
OBJECTIVE: To study the use of the Pregnancy occupational risk benefit (PORB) and non-work related sickness absence (NWSA) in a cohort of pregnant workers of Parc de Salut Mar, Barcelona (Spain). METHOD: Retrospective cohort study of 428 pregnant workers between 2010 and 2014, who were followed-up until delivery. Absences from work, both PORB and NWSA were recorded until the beginning of their maternity leave. The sequence analysis identifies four trajectories, which are described according to workers demographic and job characteristics. RESULTS: Of the total cohort, 56 (13.1%) accessed only the PORB, representing 6.126 days of absence; 68 (15.9%) also accessed PORB, with 7.127 days of absence, but had previously accumulated 102 episodes of NWSA with 1.820 days of absence. The majority of pregnant workers in the sample (69.9%) took only one or several episodes of NWSA without using PORB, with 545 episodes and 26,337 days of absence. Most were active during the first quarter and it is from the second quarter that episodes of long-term NWSA appeared. During the last month of pregnancy more than 80% of the workers were absent from work. CONCLUSIONS: Pregnant workers remained at work for two thirds of their pregnancy. Absences were mainly due to episodes of NWSA. PORB represented one third of them. As in other similar countries, our results suggest a change in the management of social protection benefits for pregnant workers.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".