Transforming School-Based Mental Health to Heal the Collective Soul Wound
Bibliographic record
Abstract
Pervasive well-being concerns of youth in Alberta are steadily contributing to society’s collective soul wound. In response to this growing need, K-12 systems are faced with increased demands for school-based mental health services. Public Prairie School Division (PPSD) provides student mental health intervention needs through onsite access to school-based teacher counsellors and referrals to centralized psychologists. However, decisions regarding mental health practitioner allocations or practice standards are often left to individuals and generally follow historical practice. This Organizational Improvement Plan (OIP) problematizes PPSD’s lack of system-wide approaches to mental health interventions that can provide assurance of improved efficacy and equity in meeting student mental health needs. Transformative leadership applied within a utilitarian consequentialist lens has the potential to improve individual and collective student well-being. The decolonizing lens of scarring the collective soul wound will elevate system leadership to counter pervasive neoliberalism and allow for change and healing within ethical spaces. Actioning psychosocial change using transformative learning theory positions practitioners as co-creators of new counselling practice standards in response to student and parent feedback. Allocation changes stemming from systemic analysis of demographic and referral data should increase equity of access to teacher counsellors. Evidence of improved access and efficacy in mental health interventions will be sought through interconnected plan-do-study-act cycles and more broadly confirmed through a RE-AIM framework. Verifying PPSD’s collective soul wound scar also requires the application of an Indigenous wellness perspective.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".