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Record W3093723567 · doi:10.3233/prm-190604

How children and their parents value using the Canadian Occupational Performance Measure (COPM) with children themselves

2020· article· en· W3093723567 on OpenAlexaboutno aff
Gijs Verkerk, Lisanne van der Molen-Meulmeester, Mattijs W. Alsem

Bibliographic record

VenueJournal of Pediatric Rehabilitation Medicine · 2020
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMeasure (data warehouse)Value (mathematics)PsychologyMedicineStatisticsComputer scienceMathematicsData mining

Abstract

fetched live from OpenAlex

PURPOSE: Although the Canadian Occupational Performance Measure (COPM) is used with children, it is unclear how they and their parents experience this. This study aims to investigate the opinions of children and their parents about the COPM when it is used with children. METHODS: Semi-structured interviews were performed with 23 children varying in age between 8 and 18 years. The transcripts of the interviews were analysed using MAXQDA software to discover overarching themes. Parents' responses to an eight-item multiple-choice questionnaire were analysed using SPSS software. RESULTS: Five themes extracted from the interviews with the children show: My way of doing the COPM; The COPM shows my own problems and wishes for change; The COPM is important for identifying the support I need; The influence of my parents and my therapist; and The COPM is suitable for me. The children experienced the COPM as a valuable tool for determining and measuring the impact of an intervention. The parents experienced the COPM as suitable for their child and judged that the child's scores were useful for showing the outcome of an intervention. CONCLUSION: Both the children and their parents valued the COPM as an outcome measure 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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.072
GPT teacher head0.391
Teacher spread0.319 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations13
Published2020
Admission routes1
Has abstractyes

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