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Record W4386989363 · doi:10.1093/pch/pxad055.026

26 How Are We Addressing Health Equity in the Paediatric Tertiary Care Setting? An Environmental Scan of Approaches across Canada

2023· article· en· W4386989363 on OpenAlexaboutno aff
Mariam Naguib, Patricia Li, Juliette St‐Georges, Catherine Korman, Annie Chabot, Rislaine Benkelfat

Bibliographic record

VenuePaediatrics & Child Health · 2023
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsnot available
Fundersnot available
KeywordsHealth equityEquity (law)Thematic analysisHealth carePublic relationsMedicineHealth policyNursingGrey literatureSocial determinants of healthPolitical scienceMEDLINEQualitative researchPublic healthSociology

Abstract

fetched live from OpenAlex

Abstract Background Increasingly, paediatricians are asserting the urgent need for effective practices and policies to address child health inequities. Yet there is a paucity of data on the current approaches implemented in Canadian paediatric tertiary care centres. According to the Institute of Health Improvement (IHI), health equity is paramount for a just society that moves toward better well-being for all. The IHI framework lists five priorities to guide health equity initiatives: make health equity a strategic priority; develop structures and processes to support health equity work; deploy specific strategies to address the multiple determinants of health on which health care organizations can have a direct impact; decrease institutional racism within the organization; and develop partnerships with community organizations. Objectives The objectives of this study were to: 1) Map out current initiatives addressing health equity, and 2) Explore the facilitators and barriers to achieving health equity, in the tertiary paediatric health care setting in Canada. Design/Methods We conducted an environmental scan using in-depth, semi-structured interviews as well as a review of the grey and published literature. We interviewed at least one paediatric resident and one staff (including paediatricians, allied health professionals, and administrators involved in health equity) at all 17 paediatric tertiary care centres in Canada. The interview guide was structured using the IHI framework, to characterize initiatives, as well as facilitators and barriers, to address health equity. We performed a thematic analysis using NVivo. Codes were generated using an inductive and deductive approach, from which themes were identified, refined, and finalized. Results We conducted 41 interviews from Fall 2021 to Fall 2022. Recent events, including Black Lives Matter and the COVID pandemic, fuelled health equity initiatives at the hospital- and university-levels. Most institutions were in a reactionary state, with few having formal strategic approaches to address inequities. The initiatives served a variety of priority populations: Indigenous, Black, migrants, rural communities, and children with disabilities, with the focus differing based on geography and the unique challenges faced by each institution. To achieve equity throughout an organization, equity at the staffing level was identified as being a necessity. The barriers to implementing health equity initiatives included lack of accountability, lack of leadership support, limited resources, and lack of institutional awareness. The facilitators included leadership support, health equity champions, and increasing awareness and resources. Conclusion A myriad of health equity initiatives are taking place in Canadian paediatric tertiary care centres, which are evolving.

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 imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.274
Threshold uncertainty score0.842

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.013
Science and technology studies0.0340.026
Scholarly communication0.0120.004
Open science0.0030.010
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.091
GPT teacher head0.384
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations1
Published2023
Admission routes1
Has abstractyes

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