Key considerations for applying intersectionality theory to partner and stakeholder engagement in public health
Bibliographic record
Abstract
SETTING: Partner and stakeholder engagement (PSE) in public health that involves tokenistic and performative practices results in further marginalization of priority populations. Inclusive, intentional, and mutually respectful engagement requires an intersectional approach to account for the overlapping and compounding impacts of multiple systems of power on the lived experiences of priority populations. However, there is a lack of practical guidance on methods, strategies, or approaches for how public health initiatives can meaningfully engage with priority populations. INTERVENTION: An evidence synthesis on intersectional approaches to PSE in public health was conducted to inform the development of an evidence-informed tool. Engagement approaches were evaluated based on (1) integration of intersectionality, as defined by the Intersectionality-Based Policy Analysis Framework and (2) level of stakeholder engagement achieved, from communication to co-production. OUTCOMES: The resulting tool offers "key considerations" for incorporating intersectionality principles in PSE in public health, encouraging critical reflection on the who, why, and how of PSE. Organized by the development, implementation, and monitoring and evaluation phases of public health initiatives, these considerations guide users through critical reflection by posing open-ended questions. IMPLICATIONS: The tool's "key considerations" are relevant for all public health practitioners, with an emphasis on those in public health institutions. It guides users in navigating structural and interpersonal power imbalances with systemically marginalized priority populations. Adopting an intersectional lens enhances the ability to identify and address the complex array of determinants of health, tailored to population-specific needs and priorities.
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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 | 0.250 | 0.200 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.015 | 0.126 |
| Scholarly communication | 0.033 | 0.059 |
| Open science | 0.010 | 0.045 |
| Research integrity | 0.013 | 0.022 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".