Centering equity and lived experience: implementing a community-based research grant on cannabis and mental health
Bibliographic record
Abstract
BACKGROUND: Mental health research in Canada is not only underfunded but there remains an inequitable distribution of funding to address unmet needs especially in clinical and applied research. In 2018, the legalization of cannabis for non-medical use in Canada sparked the need to examine the relationship between cannabis use and mental health. The federal government allocated $10 M over 5 years to the Mental Health Commission of Canada (MHCC), a pan-Canadian health organization funded at arm's length by the federal government. METHODS: In 2020, the MHCC implemented an innovative community-based research (CBR) program to investigate this relationship among priority populations including people who use cannabis and live with mental illness, First Nations, Inuit and Métis, two-spirit, lesbian, gay, bisexual, trans and/or queer (2SLGBTQ+) individuals, and racialized populations. Extensive consultations, a scoping review and an environmental scan set the research agenda. Key program components included a review committee with representation from diverse priority populations, extensive proposal-writing support for applicants, and capacity bridging workshops for the 14 funded projects. RESULTS: Of the 14 funded research projects, 6 focus on and are led by Indigenous communities, 5 focus on other equity-seeking populations, and 9 explore the perceived patterns, influence and effects of use including benefits and harms. Lessons learned include the importance of a health equity lens and diverse sources of knowledge setting the CBR research agenda. In addition to capacity bridging that promote equitable roles among knowledge co-producers as well as the critical role of organizational support in increasing research productivity, especially in the area of mental health and cannabis use where there is a need for more applied research. CONCLUSION: Centering equity and lived and living experience strengthened the rationale for investments and ensured user-led evidence generation and utilization - a key public health gain. Organizational support for proposal development and capacity bridging yields significant value that can be replicated in future CBR initiatives.
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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 | 0.182 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.014 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.010 | 0.047 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".