An intersectionality-based policy analysis framework: critical reflections on a methodology for advancing equity
Bibliographic record
Abstract
INTRODUCTION: In the field of health, numerous frameworks have emerged that advance understandings of the differential impacts of health policies to produce inclusive and socially just health outcomes. In this paper, we present the development of an important contribution to these efforts - an Intersectionality-Based Policy Analysis (IBPA) Framework. METHODS: Developed over the course of two years in consultation with key stakeholders and drawing on best and promising practices of other equity-informed approaches, this participatory and iterative IBPA Framework provides guidance and direction for researchers, civil society, public health professionals and policy actors seeking to address the challenges of health inequities across diverse populations. Importantly, we present the application of the IBPA Framework in seven priority health-related policy case studies. RESULTS: The analysis of each case study is focused on explaining how IBPA: 1) provides an innovative structure for critical policy analysis; 2) captures the different dimensions of policy contexts including history, politics, everyday lived experiences, diverse knowledges and intersecting social locations; and 3) generates transformative insights, knowledge, policy solutions and actions that cannot be gleaned from other equity-focused policy frameworks. CONCLUSION: The aim of this paper is to inspire a range of policy actors to recognize the potential of IBPA to foreground the complex contexts of health and social problems, and ultimately to transform how policy analysis is undertaken.
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 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 | 0.209 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.020 | 0.149 |
| Scholarly communication | 0.033 | 0.047 |
| Open science | 0.008 | 0.026 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.009 | 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".