Bridging the gap between professionals and the community in mental health services: findings and policy implications of two demonstration projects
Bibliographic record
Abstract
This paper describes two projects which targeted citizens as key players in the well-being and mental health of local communities and have tapped the mutual aid resources of informal helping networks. One of these projects was implemented with people who had mental health problems, many of whom were homeless and dependant drug users, in Quebec's inner city. Two professional workers, who were experienced in neighbourhood interventions (a psychologist and an educator) and a researcher were employed for a period of 28 months (from September 1989 to December 1991). The other project was implemented within the context of primary care for a wide range of health and social problems in a rural LCCS (Local Centre for Community Services). A social worker, who specialized in rural network intervention, was employed for 2 years. The same research team worked on both of the projects. The main objective of both projects was the development and testing of a method of intervention which aimed to encourage citizen involvement, both in promoting the physical and mental health of those suffering from transitory problems, and in the rehabilitation process of those suffering from severe social or mental health problems. In order to accomplish that objective, the professional workers made themselves visible and accessible in the community. The projects generated two very different models of intervention. The inner city model of intervention was tied closely to pivotal citizens and placed a great emphasis on the helper-therapy principle. The rural model was founded on network intervention, mutual aid being more relevant for marginal people. Even though these models of intervention embody provincial and federal government policies, professionals are far from ready and able to change their practice accordingly.
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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 | 0.020 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".