Community Input and Rural Mental Health Planning Listening to the Voices of Rural Manitobans: Using Community Input to Inform Mental Health Planning at the Regional Level
Bibliographic record
Abstract
Clients of mental health services in rural and northern areas of Canada encounter a myriad of challenges in accessing high quality services. These challenges include stigma and confidentiality concerns, limited resources, transportation barriers, and heightened rates of professional turnover. Fortunately there are some promising and innovative approaches (e.g., computer-based treatment, internet discussion groups, group-based programming, telehealth, telephone counseling, stepped care, collaborative mental health care) that may prove useful at addressing some of these challenges. Nonetheless, these resources must be accessed by clients in order to be effective. The current study used mail-out surveys to gather information from over 1600 residents in two large rural Manitoba health regions regarding their preferences for (1) accessing mental health information (e.g., searching the internet, reading books, accessing information from various professionals) and (2) treatment delivery options (e.g., group-based services, internet discussion groups, computer-based treatment, telephone counseling), as well as (3) perceived barriers (e.g., stigma, confidentiality, transportation) and facilitators to accessing treatment. These data are presented within the context of informing regional mental health policy with respect to such issues as allocation of mental health funding, adoption of an effective mental health resource development plan, and adoption of an effective mode of mental health care. Keywords: mental health services, regional mental health policy, accessing mental health information, treatment delivery options
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".