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Record W4281730328 · doi:10.1080/01942638.2022.2079392

Instructions for Administering the Canadian Occupational Performance Measure with Children Themselves

2022· article· en· W4281730328 on OpenAlexaboutno aff
Gijs Verkerk, Lisanne van der Molen-Meulmeester, Margo van Hartingsveldt, Mattijs W. Alsem

Bibliographic record

VenuePhysical & Occupational Therapy In Pediatrics · 2022
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
Fundersnot available
KeywordsOccupational therapyIntervention (counseling)PsychologyDelphi methodMedicinePhysical therapyNursingComputer science

Abstract

fetched live from OpenAlex

AIMS: The Canadian Occupational Performance Measure (COPM) can be used to support children to clarify their needs themselves. However, for pediatric occupational therapists it is not sufficiently clear how to effectively use the COPM with children from 8 years of age.This study aimed to formulate specific instructions for using the COPM with children themselves, based on the experience of children, parents, and occupational therapists. In addition, professional consensus on the instructions was reached. METHODS: A multi-stage approach was used to develop the instructions. Triangulation of methods was used to gather knowledge of how the COPM with children themselves is performed in daily practice: interviews with 23 children, questionnaires completed by 30 parents, interviews with 13 therapists, and 10 video recordings of COPM administration. Specific instructions were derived from this knowledge and consensus for these instructions was reached by Delphi method. RESULTS: The data were analyzed and resulted in 40 specific instructions. Consensus of at least 80% amongst 10 occupational therapists, who regularly use the COPM with children, was achieved on each instruction. CONCLUSION: There is consensus on 40 specific instructions for administering the COPM with children. Following these instructions might help children to formulate their own goals for intervention.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.118
GPT teacher head0.439
Teacher spread0.320 · 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

Citations14
Published2022
Admission routes1
Has abstractyes

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