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Record W4379986136 · doi:10.1080/19411243.2023.2221688

Children’s Occupations: Enhancing the Representation of the Paediatric Activity Card Sort

2023· article· en· W4379986136 on OpenAlexaff
Katelyn Jutzi, Julia Adeney Thomas, Helene J. Polatajko, Jane A. Davis, Tatiana Barcelos Pontes

Bibliographic record

VenueJournal of Occupational Therapy Schools & Early Intervention · 2023
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsSouthlake Regional Health CenterUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsPsychological interventionSample (material)Reliability (semiconductor)Representation (politics)PsychologysortMedical educationMedicineDevelopmental psychologyComputer sciencePsychiatryDatabase

Abstract

fetched live from OpenAlex

The Paediatric Activity Card Sort (PACS) captures children’s occupational repertoires; however, it is based on a small sample. A research was conducted to increase the sample size of the PACS; examine alternative form reliability of the e-PACS; explore the relationships among sex, grade, language spoken, and school attended, and children’s occupational repertoires. Fifty five children completed the e-PACS; seven also completed the paper version. Children reported doing on average 69.4% of the activities, with personal care the highest (89.3%). No significant differences were found among variables and PACS categories. However, children in public school reported doing more hobbies (p = 0.04) than children at catholic school. Some significant differences were found between boys and girls. The e-PACS had strong alternate form reliability with the PACS. Increasing the sample size of the PACS augments its usefulness providing school-based therapists with a tool to capture children’s occupational repertoires, identify potential participation issues and provide effective, individualized interventions that support academic support and overall wellbeing.

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)0.005
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 score0.028
Threshold uncertainty score0.703

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.159
GPT teacher head0.501
Teacher spread0.342 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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