Synthesis : Pulling It All Together
Bibliographic record
Abstract
What the Women's Health Surveillance Report AchievesThis gender-focused Women's Health Surveillance Report is the initial step in developing an effective, sustainable women's health surveillance system in Canada.This report identifies key data gaps in existing national surveys, gaps that must be addressed in order to have an effective women's health surveillance system.It uses data from a variety of national administrative and survey databases to explore sex and gender differences in important areas of women's health.While these data have often been considered "sterile" (they were collected for other purposes and generally lack much of the context needed for gender analysis), the report's authors have used them to provide some insights into disparities in the distribution of determinants of health, health behaviours, health outcomes, and health care utilization for Canadian women, and to identify vulnerable subgroups of women.This report provides a baseline for monitoring health outcomes, health-related behaviours, and other social and economic issues that affect women's lives.Its focus is a range of health issues that emerged from national consultations with women's health experts.
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 | 0.076 | 0.334 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.065 | 0.008 |
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".