Designing a Mental Health Strategy for System-Wide Changes: A National Sport Organization’s Roadmap
Bibliographic record
Abstract
This project focuses on the first phase of a multi-year project carried out with a national sport organization (NSO) to design a system-wide mental health (MH) strategy to improve well-being across its ecosystem. The collaborative work was carried out with Tennis Canada (TC). It was guided by two overarching questions: (a) How does TC design a sport-specific MH strategy, and (b) What priorities, objectives, and recommended actions from the national MH Strategy does TC include in its personalized strategy? Informed by a Participatory Action Research (PAR) approach, a representative group of 21 members from TC’s community formed a Task Force (TF), led by a Core Leadership Team (CLT), to create the strategic plan over an 11-month period. The TF and CLT engaged in four formal meetings and completed a Needs/Gap Assessment to identify which elements within the national MH Strategy were most relevant to TC. Both qualitative and quantitative data were collected, analyzed, discussed, and integrated by the participants to generate the five priorities, 18 objectives, and 45 actions included in TC’s strategy. This project demonstrates how sport psychology practitioners (SPPs) can work with a national sport organization to develop a comprehensive strategic roadmap to improve MH outcomes.
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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 | 0.037 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".