Enhancing the capacity of the mental health and substance use health workforce to meet population needs: insights from a facilitated virtual policy dialogue
Bibliographic record
Abstract
BACKGROUND: Timely knowledge mobilization has become increasingly critical during the COVID-19 pandemic and complicated by the need to establish or maintain lines of communication between researchers and decision-makers virtually. Our recent pan-Canadian research study on the mental health and substance use health (MHSUH) workforce during the pandemic identified key policy barriers impacting this essential workforce. To bridge the evidence-policy gap in addressing these barriers, we held a facilitated virtual policy dialogue. This paper discusses the insights generated at this virtual policy dialogue and highlights how this integrated knowledge mobilization strategy can help drive evidence-based policy in an increasingly digital world. METHODS: We held a 3-hour virtual policy dialogue with 46 stakeholders and policy decision-makers as the final phase in our year-long mixed-methods research study. The event was part of our integrated knowledge mobilization strategy and was designed to generate stakeholder-driven policy implications and priority actions based on our research findings. The data collected from the virtual policy dialogue included transcripts from the small-group breakout rooms and main sessions, reflective field notes and the final report from the external facilitator. Coded data were thematically analysed to inform our understanding of the prioritization of the policy implications and action items. RESULTS: Facilitated virtual policy dialogues generate rich qualitative insights that guide community-informed knowledge mobilization strategies and promote evidence-informed policy. Our policy dialogue identified actionable policy recommendations with equity as a cross-cutting theme. Adapting policy dialogues to virtual formats and including technology-assisted facilitation can offer advantages for equitable stakeholder participation, allow for deeper analysis and help build consensus regarding evidence-based policy priorities. CONCLUSIONS: Our facilitated virtual policy dialogue was a key knowledge mobilization strategy for our research on the capacity of the Canadian MHSUH workforce to respond to the COVID-19 pandemic. Our policy dialogue allowed us to engage a diverse group of MHSUH workforce stakeholders in a meaningful action-oriented way, provided an avenue to get feedback on our research findings, and generated prioritized action items that incorporated the knowledge and experience of these MHSUH workforce stakeholders.
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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 | 0.018 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.012 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".