Perspectives on Health Equity in Canadian Pediatric Academic Hospitals: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Researchers and health care professionals are increasingly asserting the urgent need for effective practices and policies to address child health inequities. However, there is a paucity of data on current approaches implemented in pediatric hospitals. We sought to explore health care professionals' and interest holders' perspectives on addressing health equity in Canadian pediatric academic hospitals. METHODS: We conducted an interpretative descriptive qualitative study involving semi-structured interviews with a purposive and snowball sample of pediatrics residents, staff pediatricians, allied health professionals, and key stakeholders working at pediatric academic hospitals in Canada. We conducted interviews via video conferencing between 2021 and 2023 and analyzed data using reflexive thematic analysis. RESULTS: We interviewed at least 1 pediatric resident and 1 staff member (including pediatricians, allied health professionals, and key stakeholders involved in health equity) at all 17 pediatric academic hospitals in Canada. Through the 42 interviewees, the following 5 main themes (along with 12 subthemes) emerged: (1) health equity was evolving across institutions; (2) institutions were navigating reactive and proactive approaches; (3) there were siloed efforts and missed opportunities for enhanced collaboration; (4) there were gaps and ethical considerations in health equity data collection; and (5) to achieve health equity throughout an organization, institutional representation and engagement was a necessity. Barriers and facilitators to health equity work were identified, as well as examples of applicable interventions. CONCLUSIONS: There were a myriad of health equity interventions that exist in Canadian pediatric hospitals, although they were evolving and maturing. Learnings can be shared across institutions.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.040 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".