Assessing the Social Impact of Mental Health Service Accessibility by a Nonprofit Social Enterprise: A Mixed--methods Case Study
Bibliographic record
Abstract
The following study sought to examine the social impact of a social enterprise mental health services model by assessing its impact on service accessibility and mental health stigma. A novel approach to case study – a mixed methods design was developed by collecting data from service users, counsellors, and community members of a social enterprise in Toronto, Ontario, using qualitative interviews and the Mental Health Knowledge Schedule (MAKS) survey. Findings show how the social enterprise increases service access and challenges mental health stigma by engaging in a variety of activities, including providing low--cost counselling, diversifying services, offering a positive and safe non--clinical environment, and engaging with the public directly by utilizing a storefront model. As a result of data triangulation analysis, common themes and discrepancies between respondent groups are identified and discussed. No significant relationships were found between mental health stigma and community member demographic characteristics. Insights on replication of this social impact assessment model are discussed.
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How this classification was reachedexpand
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 | 0.017 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.012 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".