Inclusion of intersectionality in studies of immunization uptake in Canada: A scoping review
Bibliographic record
Abstract
INTRODUCTION: Intersectionality refers to the interconnectedness of various social locations creating unique experiences for individuals and groups, in the context of systems of privilege and oppression. As part of immunization coverage research, intersectionality allows healthcare professionals and policymakers to become aware of the constellation of characteristics contributing to low vaccine uptake. The objective of this study was to examine the application of intersectionality theory or concepts, and the appropriate use of sex and gender terminology, in Canadian immunization coverage research. MATERIALS AND METHODS: The eligibility criteria for this scoping review included English or French language studies on immunization coverage among Canadians of all ages. Six research databases were searched without date restrictions. We searched provincial and federal websites, as well as the Proquest Dissertations and Theses Global database for grey literature. RESULTS: Of 4725 studies identified in the search, 78 were included in the review. Of these, 20 studies included intersectionality concepts, specifically intersections of individual-level characteristics influencing vaccine uptake. However, no studies explicitly used an intersectionality framework to guide their research. Of the 19 studies that mentioned "gender", 18 had misused this term, conflating it with "sex". CONCLUSIONS: Based on our findings, there is an evident lack of intersectionality framework utilization in immunization coverage research in Canada, as well as misuse of the terms "gender" and "sex". Rather than only focusing on discrete characteristics, research should explore the interaction between numerous characteristics to better understand the barriers to immunization uptake in Canada.
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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 | 0.084 | 0.262 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.055 | 0.081 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".