8273616 Pregnancy at work, the protection program in the province of Québec (Canada) and the experience of putting in place processes for national guidelines elaboration
Bibliographic record
Abstract
Objectives The Safe maternity experience program (SMEP) has existed since the 1979 Occupational Health and Safety Act in the province of Québec, Canada. Under SMEP, pregnant or breastfeeding workers can be assigned to another position or other tasks if exposed to an occupational risk factor. If such an assignment is not possible, the worker is entitled to preventive withdrawal with an income compensation. Specific preventive protocols, by occupational category, to be applied in the province are required by a law change since 2023. Our objective was to develop a valid and credible process to translate available knowledge into applicable recommendations to include in the protocols. Material and Methods Major limitations arising from the nature and quality of available epidemiological data were described from literature syntheses about, among others, pregnancy and standing position: discrepancies in study designs, exposure measurements, control of confounding factors and overall conclusions. Areas of consensus and discordance in current SMEP recommendations were identified with an inventory. Specific questions that require an answer for translating knowledge into recommendations were then formulated by our scientific team and validated by experts who wrote or used previous SMEP guidelines. Processes used by other organizations that make decisions in similar uncertainty context also were reviewed. Results Risk management approach based on predefined criteria was favored with a drafted deliberative process to make informed conclusions. A multidisciplinary committee was created including various clinical specialists, ethician, labor law lawyer, etc., and working women, to combine epidemiological data, prepared by the scientific team, with experiential and contextual information held by the committee members. A mechanism to refine questions and criteria after the first deliberations was established. Conclusion Risk management is a promising approach to combine all available data in a deliberative process pertaining complex questions such as translating knowledge into preventive guidelines for a safe maternity at work.
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 | 0.046 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.002 |
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".