Aiming for transformations in power: lessons from intersectoral CBPR with public housing tenants (Québec, Canada)
Bibliographic record
Abstract
Intersectoral collaborations are recommended as effective strategies to reduce health inequalities. People most affected by health inequalities, as are people living in poverty, remain generally absent from such intersectoral collaborations. Community-based participatory research (CBPR) projects can be leveraged to better understand how to involve people with lived experience to support both individual and community empowerment. In this paper, we offer a critical reflection on a CBPR project conducted in public housing in Québec, Canada, that aimed to develop intersectoral collaboration between tenants and senior executives from four sectors (housing, health, city and community organizations). This single qualitative case study design consisted of fieldwork documents, observations and semi-structured interviews. Using the Emancipatory Power Framework (EPF) and the Limiting Power Framework (LPF), we describe examples of types of power and resistance shown by the tenants, the intersectoral partners and the research team. The discussion presents lessons learned through the study, including the importance for research teams to reflect on their own power, especially when aiming to reduce health inequalities. The paper concludes by describing the limitations of the analyses conducted through the EPF-LPF frameworks and suggestions to increase the transformative power of future studies.
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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 | 0.031 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.054 | 0.025 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".