1 CREATING TOGETHER: DEVELOPING A MENTAL HEALTH AND ADDICTIONS RESEARCH AGENDA FOR ONTARIO
Bibliographic record
Abstract
In Ontario, as in many other jurisdictions, the mental health and addiction systems research agenda is driven mainly by funding bodies and scientists with little opportunity given to stakeholders to provide input. There are several challenges to this approach. The research needs of stakeholders who are in the best position to use research to improve the system are not generally addressed, the knowledge that stakeholders have of the system is not used to inform the agenda, and there is less likelihood that research will be integrated and applied by stakeholders to enhance the system once studies are complete. Academics note that the best predictor of the use of research in the field is early and continued involvement of decision makers or stakeholders (Lavis et al., 2003). It makes sense then that stakeholder collaboration should begin during the early stages of developing relevant research questions (Lomas at al., 2003). The involvement of stakeholders in establishing a jointly created research agenda is a critical step towards creating a more efficient and sustainable mental health and addictions system. Several initiatives to involve stakeholders in the development of a research agenda have already taken place in different Canadian jurisdictions to address the challenges outlined above. At the national level, the Canadian Health Services Research Foundation (CHSRF)
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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 | 0.025 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.033 | 0.011 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.009 | 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".