16.E. Workshop: Preventing public health interventions from contributing to social inequalities in health
Bibliographic record
Abstract
Abstract Reasons Public health aims to improve the health of populations and reduce social inequalities in health, notably through action on social determinants of health, physical and social environments, public policies, access to health services, and community empowerment. As such, public health has a social agenda and therefore a corresponding social responsibility. However, social inequalities in health continue to be a pressing problem in Canada, as well as around the world. Growing research demonstrates that public health interventions can unintentionally contribute to increasing social inequalities in health by perpetuating social norms that stigmatize vulnerable groups, neglecting the needs of vulnerable groups, and/or replicating power dynamics that reinforce situations which disenfranchise vulnerable populations. Objectives This workshop aims to: 1) discuss how public health interventions can inadvertently contribute to increasing social inequalities in health; 2) explore strategies to develop more equitable interventions. Added value Despite growing research on the unintended contributions of public health interventions in increasing social inequalities in health, this pressing problem remains generally under acknowledged in public health research and practice. Yet in order to reverse these effects, researchers and professionals alike need to become aware of and reflect on the potential impacts of their actions on vulnerable populations. Thus, the presentations in this workshop will provide concrete examples based on innovative research findings and critical reflections of the ways in which public interventions can increase social inequalities in health. We will also suggest health equity-related considerations for future intervention evaluation and design. Coherence The two first presentations will serve to illustrate how public health interventions can increase social inequalities in health and how we might reverse these effects by drawing on examples from tobacco control and health care. Based on these examples, the last presentation will demonstrate how a theoretically-driven framework (i.e. Acting Within Contexts) can be applied for intervention evaluation and future equitable intervention design. Format A brief introduction to present the workshop topic and learning objectives (5 minutes) Three presentations by panelists (10 minutes each; 30 minutes total): The unintended effects of tobacco control policies on social inequalities in smoking: Moving forward with a health equity approach; Equality versus equity: Barriers to health care access for Indigenous populations in Canada; Applying the “Acting Within Contexts” framework to intervention evaluation and equitable intervention design. A plenary discussion with the audience to draw collective lessons (25 minutes) Key messages To raise awareness of the potential unintended effects of public health interventions on increasing social inequalities in health. To better understand how to reduce inequities by integrating the needs and contexts of vulnerable populations in intervention planning.
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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 | 0.024 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.054 | 0.017 |
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".