Outsourcing Mental Health Programs: Harms to Public Education and to Students
Bibliographic record
Abstract
Students are experiencing high levels of stress and mental health distress and are at greater risk of suicide, resulting in calls to provide appropriate mental health supports in schools. In response, provincial governments are outsourcing K–12 mental health supports to private organizations (both non- and for-profit). Through a review of Manitoba education documents, we traced over 50 private organizations recommended by the provincial government and over $8.9 million of public money spent on these programs. Situated within the broader neo-liberal trend of the privatization of public education, we then used a critical policy analysis approach to analyze these programs’ content, explicating the ways in which these outsourced programs endorse the deprofessionalization of the teacher and the self-responsibilization of students while enlisting problematic content. We argue that outsourcing ultimately undermines education as a public good and recommend holding governments accountable, developing research-informed mental health supports, and implementing a critical assessment process when considering outsourcing to private organizations.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".