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Record W4417523800 · doi:10.1093/pch/pxaf123

Evaluation of an interprofessional school-based health centre: A multimethods study

2025· article· en· W4417523800 on OpenAlexaff
Kathleen Morgan, Jessy Gatete, Mary Hendrickson, Hugues Plourde, Noa Hitterman, Reina Remman, David Estok, Geoffrey Dougherty, Matthew Donlan, David D’Arienzo

Bibliographic record

VenuePaediatrics & Child Health · 2025
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsMontreal Children's HospitalMcGill University Health CentreMontreal Heart InstituteMcGill University
Fundersnot available
KeywordsHealth careInterprofessional educationMEDLINEMental healthChartPublic healthScale (ratio)

Abstract

fetched live from OpenAlex

Objectives: School-based health centres (SBHCs) aim to improve access to care, especially among socially vulnerable children. Interprofessional SBHCs are emerging, though their evaluations remain limited. We evaluated an interprofessional SBHC in a low-income community. Methods: The evaluation of the SBHCs was guided by the RE-AIM framework. The multimethods design included (i) a document analysis and (ii) a retrospective chart review of SBHC patients from September 2021 to May 2024. Results: The SBHC team comprised of paediatricians, a dietitian, occupational therapist, speech language pathologist, and a nurse working alongside school staff and community providers. After 98 clinic half-days, 760 appointments were scheduled across 153 patients, with a nonattendance rate of 12%. Care needs extended beyond preventative medicine for 80% of children, with mental health conditions being the most common diagnoses. Conclusion: This evaluation demonstrates that interprofessional SBHCs are feasible and offer a scalable model for addressing the complex health needs of socially vulnerable children.

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)0.029
metaresearch head score (Gemma)0.025
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.153

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.065
GPT teacher head0.486
Teacher spread0.421 · 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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