General Practice and Mental Health Care: Determinants of Outpatient Service Use
Bibliographic record
Abstract
OBJECTIVE: To examine the determinants that lead Canadian adults to consult family physicians, psychiatrists, psychologists, psychotherapists, and other health professionals for mental health reasons and to compare the determinants of service use across provider types. METHOD: Data from the Canadian Community Health Survey: Mental Health and Well-Being were used for people aged 18 years and older (n = 35,236). A multivariate logistic regression was used to model outpatient consultations with different providers as a function of predictive determinants. RESULT: Three types of variables were examined: need, enabling, and predisposing factors. Among need, the most common predictors of service use for mental health reasons were self-rated mental health, the presence of chronic conditions, depression and panic attacks, unmet mental health needs, psychological well-being, and the ability to handle daily demands. Among enabling factors, emotional and informational support and income were important predictors. Among predisposing factors, men were less likely to consult with a family physician and other resources but not with psychiatrists; and people with less education were less likely to consult psychologists and other health providers. CONCLUSION: Need factors were the most important predictors of both psychiatrist and combined family physician and psychiatrist consultation in the previous year. However, sex barriers remain and promotion campaigns in seeking mental health care should be aimed toward men. Further, education and income barriers exist in the use of specialty providers of psychotherapy and policies should thus focus on rendering these services more accessible to disadvantaged people.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".