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Record W4411706837 · doi:10.1177/15691861251354878

Descriptive evaluation of community based children’s occupational therapy services using COPM

2025· article· en· W4411706837 on OpenAlexaboutno aff
Caroline Mills, Annette Zucco, Kirralee Hazeltine, Jessica Sheaves, Karen P. Y. Liu

Bibliographic record

VenueHong Kong Journal of Occupational Therapy · 2025
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
Fundersnot available
KeywordsOccupational therapyHandwritingDosingMedicineRank correlationIntervention (counseling)Spearman's rank correlation coefficientPhysical therapyDescriptive statisticsService (business)Service delivery frameworkPsychologyClinical psychologyNursing

Abstract

fetched live from OpenAlex

Background: Community occupational therapy forms a critical primary health service in supporting the development of young children. This study aims to explore characteristics of service provision, parent rated outcomes and the relationship between dosing and service outcomes. Methods: A retrospective file review was completed to examine the services received by 60 children, aged 0-6 (mean age 3.8 years). Characteristics of service provision were described. Parent-reported performance and satisfaction scores of the Canadian Occupational Performance Measure (COPM) before and after the intervention were compared. Spearman rank order correlation was used to explore the relationship between intervention "dose" and therapy outcomes. Results: < 0.001). Spearman rank order correlation showed no relationship between dosing and outcomes. This study has reported the predominant service provision around handwriting and motor skills with a long wait time. Conclusion: Findings may assist in future service development, including the service to be provided, considering wait times and equity considerations. Further work is needed to explore what dosage yields the best outcomes.

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)0.009
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.464
GPT teacher head0.557
Teacher spread0.093 · 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 designObservational
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

Citations1
Published2025
Admission routes1
Has abstractyes

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