The influence of gender and other patient characteristics on health care-seeking behaviour: a QUALICOPC study
Bibliographic record
Abstract
BACKGROUND: Canadians' health care-seeking behaviour for physical and mental health issues was examined using the international Quality and Cost of Primary Care (QUALICOPC) survey that was conducted in 2013 in Canada. METHOD: This study used the cross-sectional Patient Experiences Survey collected from 7260 patients in 759 practices across 10 Canadian provinces as part of the QUALICOPC study. A Responsive Care Scale (RCS) was constructed to reflect the degree of health care-seeking behaviour across 11 health conditions. Using several patient characteristics as independent variables, four multiple regression analyses were conducted. RESULTS: Patients' self-reports indicated that there were gender differences in health care-seeking behaviour, with women reporting they visited their primary care provider to a greater extent than did men for both physical and mental health concerns. Overall, patients were less likely to seek care for mental health concerns in comparison to physical health concerns. For both women and men, the results of the regressions indicated that age, illness prevention, trust in physicians and chronic conditions were important factors when explaining health care-seeking behaviours for mental health concerns. CONCLUSION: This study confirms the gender differences in health care-seeking behaviour advances previous research by exploring in detail the variables predicting differences in health care-seeking behaviour for men and women. The variables were better predictors of health care-seeking behaviour in response to mental health concerns than physical health concerns, likely reflecting greater variation among those seeking mental health care. This study has implications for those working to improve barriers to health care access by identifying those more likely to engage in health care-seeking behaviours and the variables predicting health care-seeking. Consequently, those who are not accessing primary care can be targeted and policies can be developed and put in place to promote their health care-seeking behavior.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".