‘It doesn’t happen any other way’: relationship-building and reflexivity for equity-focused intersectoral practice (EquIP)
Bibliographic record
Abstract
Intersectoral processes that bring together public institutions, civil society organizations and affected community members are essential to tackling complex health equity challenges. While conventional wisdom points to the importance of human relationships in fostering collaboration, there is a lack of practical guidance on how to do intersectoral work in ways that support authentic relationship-building and mitigate power differentials among people with diverse experiences and roles. This article presents the results of RentSafe EquIP, a community-based participatory research initiative conducted in Owen Sound, Canada, in the midst of a housing crisis. The research explored the potential utility of equity-focused intersectoral practice (EquIP), a novel approach that invests in human relationships and knowledge co-creation among professionals and affected members of the community. The three-phase EquIP methodology centred the grounded expertise of community members with lived/living experience of housing inadequacy to catalyze reflexive thinking by people in professional roles about the institutional gaps and barriers that prevent effective intersectoral response to housing-related inequities. The research demonstrated that EquIP can support agency professionals and community members to (i) engage in (re)problematization to redefine the problem statement to better include upstream drivers of inequity, (ii) support reflexivity among those in professional roles to identify institutional practices, policies and norms that perpetuate stigma and impede effective intersectoral response and (iii) spark individual and collective agency and commitment towards a more equity-focused intersectoral system. We conclude that the EquIP methodology is a promising approach for communities seeking to address persistent health equity and social justice challenges.
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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 | 0.075 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.111 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".