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Record W3054855786 · doi:10.1093/pch/pxaa068.114

115 An Evidence-Based Model of Care for Newcomer Children with Special Health Care Needs

2020· article· en· W3054855786 on OpenAlexaffabout
Ayesha Rizwan, Shazeen Suleman

Bibliographic record

VenuePaediatrics & Child Health · 2020
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsSt. Michael's HospitalUniversity of Toronto
Fundersnot available
KeywordsThematic analysisHealth carePopulationFormative assessmentMedical homeMedicineNursingInterpreterRefugeeSpecial needsFamily medicinePsychologyQualitative researchEnvironmental healthGeographyPolitical scienceSociology

Abstract

fetched live from OpenAlex

Abstract Background In 2018, Canada resettled the most refugees in the world, in response to the greatest migration crisis in global history. The refugee and resettlement experience at critical stages of children’s development places children at risk for a number of chronic illnesses. Newcomer children with chronic illnesses or special health care needs (NCSHCN) require services and care providers across many systems, but face greater barriers to healthcare access and are at an increased risk of unmet needs, yet no research has been done to identify best practices for this vulnerable population. Objectives To develop an evidence-based model for high-quality, patient-centered care for NCSHCN and identify areas of need in a large Canadian city with a high density of newcomers. Design/Methods Using formative research design, a literature review and thematic analysis was performed to develop a conceptual model of care for NCSHCN. Next, a local environmental scan was conducted to identify and evaluate current clinics serving newcomers in a large urban Canadian city. Variables collected included the constructs identified in the conceptual model, and information about population served, providers and services offered including access to paediatrics. Results 326 studies were identified, of which 43 studies underwent full-text review and 21 were included in the final synthesis. Six key components were identified to best support NCSHCN: access to interpreters and appropriately translated resources; delivery of culturally competent care; access to care coordination and system navigation; longer appointment times; family-centered care through medical homes and home-based services; and an enhanced knowledge and understanding of health insurance processes. The environmental scan identified 50 clinics and programs serving newcomers, with 88% providing referrals to paediatric services but only 12% with a paediatrician on-site. Eighty-eight percent offered some form of interpreter services and while 71% offered patient navigation/care coordination services, only one program was specific to navigating child health services and programs. Conclusion We propose a data-driven model of care for NCSHCN that can reduce the intersecting disparities these families face by promoting equitable access to health and community services, thereby improving child outcomes and quality of life. While many programs for newcomers exist, access to paediatric services remains elusive and training in cultural competency and insurance processes is variable. More programs that integrate paediatric services into the community to make quality care more accessible and family-centered are required.

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)0.036
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.136
Threshold uncertainty score0.307

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.049
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.005
Bibliometrics0.0150.010
Science and technology studies0.0050.005
Scholarly communication0.0100.006
Open science0.0060.007
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.071
GPT teacher head0.395
Teacher spread0.325 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations0
Published2020
Admission routes2
Has abstractyes

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