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Record W3197617807 · doi:10.1177/13674935211041863

Shades of care: Understanding the needs of racially and ethnically diverse paediatric patients, their families, and health care providers in North America

2021· review· en· W3197617807 on OpenAlexaff
Raisa Ladha, Elena Neiterman

Bibliographic record

VenueJournal of Child Health Care · 2021
Typereview
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsEthnic groupEthnically diverseHealth careContext (archaeology)NursingCultural competenceMedicineQualitative researchCultural diversityFocus groupPsychologyPolitical scienceSociologyPedagogy

Abstract

fetched live from OpenAlex

While race and ethnicity have been acknowledged as determinants of health, there remain gaps regarding their effects on experiences of paediatric care. This scoping review examines empirical literature regarding the state and experience of paediatric care provided to racially and ethnically diverse families in North America. We seek to clarify the needs of care administrators and recipients, as well as to conceptualize what paediatric care must look like to enable equitable practices and optimal health outcomes. Utilizing Arksey and O'Malley's framework, we reviewed literature published between 2005 and 2020, most of which was written within an American context. The literature reviewed featured quantitative, qualitative and mixed methods studies. Paediatric care administrators and recipients collectively identified the following as domains requiring an increased focus: (1) knowledge (awareness or training), (2) alignment of views and values, (3) resources and (4) communication. Findings suggest overall that despite there being merit in the cultural competency efforts underway, more patient-centric approaches are vital. This review concludes by encouraging the sustained development of cultural safety initiatives in paediatric care to ultimately promote patient comfort and provider-patient collaboration.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.610
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.067
GPT teacher head0.394
Teacher spread0.326 · 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 teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreReview

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".

Quick stats

Citations7
Published2021
Admission routes1
Has abstractyes

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