Employing the equity lens to understand multisectoral partnerships: lessons from a mixed-method study in Canada
Bibliographic record
Abstract
Abstract BackgroundMultisectoral approaches to health are collaborations between stakeholders across multiple sectors, usually formed to combat issues that affect health but go beyond the purview of one particular sector. The significance of multisectoral partnerships to attain health equity has been widely acknowledged. However, the extent which equity can be attained depends upon the perceptions of various stakeholders. We examine how multisectoral partnerships promoting healthy eating and active living conceptualized and employed an equity lens in their work.MethodThis study is part of a larger pan-Canadian mixed-method research and knowledge sharing program entitled MUSE (Multisectoral Urban Systems for health and Equity in Canadian cities). Data collected from both quantitative and qualitative sources for two sites of the MUSE project-Saskatoon and Toronto were analyzed. In the qualitative part, 30 semi-structured key informant interviews were conducted with key stakeholders from six different multisectoral partnerships based in Saskatoon and Toronto. Data were analyzed in an inductive way. In the quantitative part, a survey with 37 stakeholder organizations was carried out. Simple descriptive statistics (means and percentages) were used to see the distribution of data and to supplement the qualitative analysis.ResultsEquity was not a central component in program design although participants addressing equity, did so by discussing accessibility. How much consideration was given to equity varied as a function of the type of partnership. Most participants emphasized geographical accessibility but a few mentioned financial accessibility. Collaborative leadership style facilitated participatory decision-making process, and thereby upheld equity in the partnership decision making process. Communication, networking, and negotiation skills were found to be core competencies of a leader that contributed in upholding equity in partnership dynamics. The study also found some challenges to embed equity in partnership works, such as the lack of comprehensive understanding of population health and the equity tenet within it.ConclusionsFindings indicate that multisectoral partnerships aimed at promoting healthy eating and physical activity struggle to attain equity within the partnership as well as in the partnership-based works aimed at reducing health equity in populations. Factors identified can support decision-makers commit to and work to attaining equity within their partnerships as well as in the partnership-based work in the community and beyond.
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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 | 0.042 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".