Bibliographic record
Abstract
HEALTH ISSUE: Total fertility rates (TFRs) have decreased worldwide. The Canadian fertility rate has gone from 3.90 per woman in 1960 to 1.49 in 2000. However, not many studies have examined the impact on women's health of reduced fertility rates, delayed fertility and more births to unmarried women. This paper presents information on the relation between family size and specific determinants of health. KEY FINDINGS: The rate of TFR decline varies considerably by geographic location and socio-demographic subgroup. Further, the associations between family size and selected determinants of health are different for women and men. For example a woman with one child is almost four times more likely to be "coupled" than a childless woman, and if she has two children she is significantly more likely to be "coupled" than if she had only one child. However, a man with one or more children is over six times more likely to be "coupled" than his childless counterpart, and this does not vary with family size. DATA GAPS AND RECOMMENDATIONS: There is a paucity of data on the impact of reduced fertility rates on women's health in general and on how women's roles affect their decision to have children. While it would be useful to examine longer-term health outcomes by parity and age of first birth, as well as socio-economic and role-related variables these longitudinal and detailed "role related" data are not available. Given the differing profiles of women and men with children, further health policies research is needed to support vulnerable women with children.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".