Bibliographic record
Abstract
HEALTH ISSUE: There are differences in health practices and self-rated health among different socio-demographic groups of women. The relationship between socio-demographic status and a) a range of health behaviours and b) a combination of multiple risk and multiple health promoting practices were examined. The relationship between self-rated health and health practices was also assessed. KEY FINDINGS: There were geographic differences in health practices with women in British Columbia having the highest odds of engaging in multiple health promoting practices, while women in Quebec had the lowest. Reports of engaging in multiple risk behaviours were most common in Ontario. Women from Ontario had the highest odds of reporting very good/excellent health and women from British Columbia had among the lowest odds.The data supported a strong social gradient between an increase in income/education and healthy practices, especially those that are health promoting. However, women with higher education were more likely to be overweight and those with higher incomes were more likely to drink alcohol regularly.Immigrant women were less likely to engage in multiple health risk practices compared to Canadian-born women. However, they were less likely to report very good/ excellent health than non- immigrants. While marriage appeared to have a generally protective effect on women's health practices, single women were more likely to be physically active and have a normal weight. DATA GAPS AND RECOMMENDATIONS: More sensitive indicators need to be developed to better understand possible reasons for the socioeconomic gradient. Data collection should focus on both rural and Aboriginal populations.
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 | 0.001 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.108 | 0.019 |
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".