Actualizing community–academic partnerships in research: a case study on rural perinatal peer support
Bibliographic record
Abstract
BACKGROUND: Within the field of patient and public involvement in health service research, there is a growing movement towards not only involving patients in research but engaging them as co-producers of knowledge. We explore such a co-productive research relationship in a case study on rural perinatal mental health, with the aim of collaboratively developing knowledge based on both the relevant lived experience of a community partner, and the systemic knowledge of academic researchers. METHODS: Data was gathered through a community forum and subsequent interviews with social service program administrators from rural British Columbia, Canada. Interviews were analyzed separately by the community partner and academic researchers using principles of thematic analysis. Both the community partner and academic researchers were involved from project genesis to data collection, analysis, interpretation, and manuscript writing. RESULTS: Common themes identified by the academic and community researchers included needs for peer support, barriers to peer support, and gaps in mental health care. Divergently, the academic researcher focused on systems-level challenges while the community partner emphasized the impact of power dynamics within health systems. Researchers generated five methodological values propositions from the process of co-production, including (a) mutual respect for all viewpoints, (b) a rejection of assumed hierarchy, (c) commitments to truth speaking, (d) attention to process, and (e) equivalence of contribution. CONCLUSIONS: Co-production highlights the value of lived experience in health research, sets it in conversation with scientific inquiry, and moves away from hierarchies of assumed knowledge often embedded in traditional health care research. Incorporating both academic researcher and community partner writing into our paper reflects a commitment to maintaining the integrity and authenticity of lived experience, an affirmation of its equal validity as a source of knowledge, and a rejection of qualifying patient voices. The exploration of this co-production research relationship lays groundwork for future research teams considering collaborative methodology. We suggest co-productive research as a means of addressing the epistemic injustice that arises in health care research from the privileging of certain forms of knowledge, and the exclusion of others, namely that derived from patient experience.
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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 | 0.106 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 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".