Determinants of Rural Women's Health: A Qualitative Study in Southwest Ontario
Bibliographic record
Abstract
CONTEXT: The influences of gender and geography are increasingly being acknowledged as central to a comprehensive understanding of health. Since little research on rural women's health has been conducted, an in-depth qualitative approach is necessary to gain a better initial understanding of this population. PURPOSE: To explore the determinants of health and their influence on rural women's health. METHODS: From November 2004 to September 2005, 9 focus groups and 3 individual interviews were conducted in 7 rural southwestern Ontario communities. Sixty-five rural residents aged 26 years and older participated in the study. Semi-structured interview questions were used to elicit participants' perceptions regarding determinants of rural women's health. FINDINGS: Four Health Canada determinants (employment, gender, health services, and social environments) and 3 new determinants (rural change, rural culture, and rural pride) emerged as key to rural women's health. CONCLUSIONS: Although health determinants affect both urban and rural people, this qualitative study revealed that rural women experience health determinants in unique ways and that rural residents may indeed have determinants of their health that are particular to them. More research is needed to explore the nature and effects of determinants of health for rural residents in general, and rural women in particular.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".