Variables Associated With Perceived Unmet Need for Mental Health Care in a Canadian Epidemiologic Catchment Area
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVES: This study identified variables associated with perceived partially met and unmet needs for information, medication, and counseling, as well as overall perceived unmet needs, related to mental health among 571 people in a Canadian epidemiologic catchment area. METHODS: Needs were measured with the Perceived Need for Care Questionnaire and a comprehensive set of independent variables based on Andersen's behavioral model. Four models were constructed for the following dependent variables: perceived unmet needs for information, medication, and counseling (multinomial logistic regression) and overall perceived unmet needs (multiple logistic regression). RESULTS: The proportions reporting fully unmet need were as follows: counseling, 30%; information, 18%; and medication, 4%. Variables associated with unmet needs for information, medication, and counseling were quite distinct. Enabling factors (for example, neighborhood perception variables) were strongly associated with perceived unmet need for information. Need factors were more strongly associated with unmet need for medication, predisposing factors with unmet needs for information and medication, and health service use with unmet information and counseling needs. People whose overall needs went unmet tended to be younger, to have an addiction, and to have consulted fewer professionals. CONCLUSIONS: Mental health services should facilitate access to psychologists or other clinicians to better meet counseling and information needs. They should also take neighborhoods into account when assessing needs and provide more information about mental disorders and the treatments and services offered in disadvantaged areas. Finally, services should be further developed for younger people with addiction, who tend to be stigmatized and avoid using health services.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it