Bibliographic record
Abstract
For my practicum, I worked with the Health Equity Integration Team (HEIT) to improve the application of Sex- and Gender-Based Analysis + (SGBA+) at The Public Health Agency of Canada (PHAC). SGBA+ is an analytical tool used in the federal government to ensure the consideration of diversity and intersectionality in programs and policies. One of the training resources on SGBA+ at PHAC is called Toward Health Equity: The SGBA+ Guide. This guide provides an overview of SGBA+, associated concepts, and a case study. I was part of a team tasked with updating this document to make the guide more applicable to current agency priorities. However, in revising the guide it became clear that there was a significant gap in understanding what document users needed. To make this guide as user-friendly and relevant as possible, I suggested that we conduct interviews with key informants throughout the agency to gather feedback and identify barriers to SGBA+ application. This project was part of a Knowledge Translation (KT) process that involved employees from many different roles and divisions at PHAC. The interviews allowed readers to identify the guide’s strengths, weaknesses, and gaps in clarity and content. Improving SGBA+ application at the federal public health level is important, because it is the agency’s way of applying a health equity lens to the work that they do. This project was also significant because it interrupted the standard process of KT, which follows a linear path and only integrates user feedback at the end. Instead, this project promoted an iterative process, involving document users throughout the development and revision of the guide to create a final product that is more tailored to their needs. Clear and effective communication is crucial to public health practice; this project is an example of how to achieve that by incorporating constructive feedback.
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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 | 0.029 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.030 | 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".