A balancing act: navigating the nuances of co-production in mental health research
Bibliographic record
Abstract
BACKGROUND: In the context of mental health research, co-production involves people with lived expertise, those with professional or academic expertise, and people with both of these perspectives collaborating to design and actualize research initiatives. In the literature, two dominant perspectives on co-production emerge. The first is in support of co-production, pointing to the transformative value of co-production for those involved, the quality of services developed through this process, as well as to broader system-level impacts (e.g. influencing changes in health system decision making, care practices, government policies, etc.). The second stance expresses scepticism about the capacity of co-production to engender genuine collaboration given the deeply ingrained power imbalances in the systems in which we operate. While some scholars have explored the intersections of these two perspectives, this body of literature remains limited. MAIN TEXT: This paper contributes to the literature base by exploring the nuances of co-production in health research. Using our mental health participatory action research project as a case example, we explore the nuances of co-production through four key values that we embraced: 1. Navigating power relations together 2. Multi-directional learning 3. Slow and steady wins the race 4. Connecting through vulnerability CONCLUSIONS: By sharing these values and associated principles and practices, we invite readers to consider the complexities of co-production and explore how our experiences may inform their practice of co-production. Despite the inherent complexity of co-production, we contend that pursuing authentic and equitable collaborations is integral to shaping a more just and inclusive future in mental health research and the mental health system at large.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.318 | 0.268 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.049 | 0.290 |
| Scholarly communication | 0.062 | 0.067 |
| Open science | 0.009 | 0.068 |
| Research integrity | 0.019 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".