Employing the equity lens to understand multisectoral partnerships: lessons learned from a mixed-method study in Canada
Bibliographic record
Abstract
BACKGROUND: Multisectoral approaches to health are collaborations between stakeholders across multiple sectors, usually formed to address 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. METHOD: This 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 representatives of stakeholder organizations was carried out. Simple descriptive statistics (means and percentages) were used to observe the distribution of data and to complement the qualitative analysis. RESULTS: Equity 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 a participatory decision-making process, and thereby upholding 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 showed some challenges to embed equity in partnership works, such as the lack of comprehensive understanding of population health and its equity tenet. CONCLUSIONS: Findings indicate that multisectoral partnerships aimed at promoting healthy eating and physical activity experience several challenges 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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".