Integrating intersectionality into child health research: Key considerations
Bibliographic record
Abstract
Child health inequities persist in Canada, particularly among sovereignty-deserving First Nations, Métis and Inuit groups and equity-deserving communities. We argue for a fundamental shift in research to remedy these inequities, via an intersectional lens that highlights how social identities and systems of power contribute to disparities. Specifically, we suggest (a) integrating intersectionality, from research conceptualization to results dissemination; (b) respectfully and reciprocally engaging with communities; (c) respectfully collecting and reporting data; (d) recognizing and explicating the diversity within social categories; (e) applying intersectional analytical approaches, and (f) using diverse, participatory and inclusive dissemination strategies. We further underscore the importance of researchers acknowledging their positionalities and their role in promoting reflexivity, as well as using equity, diversity and inclusion principles throughout the research process. We call for a collective commitment to adopt intersectional and EDI approaches in paediatric research, paving the way towards a more equitable health landscape for all children.
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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 | 0.500 | 0.413 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.026 | 0.089 |
| Scholarly communication | 0.046 | 0.051 |
| Open science | 0.012 | 0.066 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".