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Record W4387021065 · doi:10.3389/fpubh.2023.1275920

Partnering for Change: collaborating to transform occupational therapy services that support inclusive education

2023· article· en· W4387021065 on OpenAlexafffundabout
Wenonah Campbell, Cheryl Missiuna, Leah Dix, Sandra Sahagian Whalen

Bibliographic record

VenueFrontiers in Public Health · 2023
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health ResearchOntario Neurotrauma Foundation
KeywordsOccupational therapyInclusion (mineral)ObligationParticipatory action researchService delivery frameworkPerspective (graphical)Service (business)MedicineMedical educationPsychologyNursingPolitical scienceSociologyBusinessSocial psychologyComputer science

Abstract

fetched live from OpenAlex

The United Nations champions inclusive education as a moral obligation, requiring equitable learning environments that meet all individuals' diverse learning needs and abilities, including children and youth. Yet the practice of inclusive education is variable and implementation challenges persist. A participatory action research framework was used to develop a solution, Partnering for Change (P4C), which is a tiered service delivery model that bridges health and education by re-envisioning occupational therapy services and transforming the role of the occupational therapist from a service provider for individual children to a collaborative partner supporting the whole school community. This perspective article will describe the P4C model and its evolution, and will outline how it has been implemented in Canadian and international contexts to facilitate children's inclusion and participation in educational settings.

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)0.052
metaresearch head score (Gemma)0.040
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: Empirical · Consensus signal: none
Teacher disagreement score0.052
Threshold uncertainty score0.274

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0520.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0230.026
Scholarly communication0.0190.014
Open science0.0040.044
Research integrity0.0060.011
Insufficient payload (model declined to judge)0.0050.001

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 head0.322
GPT teacher head0.558
Teacher spread0.236 · 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
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

Citations23
Published2023
Admission routes3
Has abstractyes

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