The impact of provincial government funding arrangements on community-based nonprofit organizations providing mental health services
Bibliographic record
Abstract
In 1993, Manitoba Health implemented the second phase of a reform of its mental health services. One of the elements of the reform included entering into funding arrangements with nonprofit organizations to deliver services. The literature cautioned nonprofit organizations about partnering with government. In particular, there were concerns that their organizational goals would be distorted; their advocacy role diminished; their accessability reduced; their staffing configuration altered; and finally, their structure bureaucratized. Five years after implementation of the reform the writer interviewed the executive directors of 14 organizations that accepted government funding in order to assess the extent and nature of the shift and its impact on the organizations involved. The results revealed that approximately $4 million was awarded to a variety of organizations in exchange for the delivery of a wide range of services throughout the province. In the process, the provincial government became the largest single source of revenue eclipsing all other sources. Initially, the negative impacts appeared to have been minimal. In the long term, stagnating funding levels have reduced the nonprofit organization's ability to recruit and retain staff. As a result, some are reconsidering their continued involvement in the delivery of govemment-funded services.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".