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Record W4413112522 · doi:10.1177/15394492251360232

Minimal Important Change in Canadian Occupational Performance Measure: A Systematic Review

2025· review· en· W4413112522 on OpenAlexaboutno aff
Hiroshi Yuine, Takeshi Sasaki, Kazuhiro Miyata, Sawako Saito, Hideki Shiraishi

Bibliographic record

VenueOTJR Occupational Therapy Journal of Research · 2025
Typereview
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsMeasure (data warehouse)Systematic reviewPsychologyComputer scienceMEDLINEPolitical scienceData mining

Abstract

fetched live from OpenAlex

The Canadian Occupational Performance Measure (COPM) recognizes a significant clinical shift with a minimum two-point alteration post-intervention. However, there is sparse evidence supporting this criterion's clinical significance. To investigate and report minimal important change (MIC) and minimal detectable change (MDC) within the COPM in various populations. In May 2023, a search was conducted to locate studies that calculated MIC and MDC in the COPM using multiple databases. Studies were screened and extracted to assess bias risk in the final article selections. After screening 229 included studies, five studies were selected; four studies calculated MIC (performance: 0.20-3.20 points, satisfaction: 1.45-3.20 points) and one study calculated MDC (performance: 1.47-3.14 points, satisfaction: 1.80-3.98 points) within the COPM. The study populations, reassessment periods, and risk of bias varied. To effectively use COPM's MIC and MDC as references, clients and intervention conditions must be considered.

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)0.014
metaresearch head score (Gemma)0.063
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.072
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.063
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0090.007
Bibliometrics0.0120.012
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.467
GPT teacher head0.611
Teacher spread0.144 · 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 designSystematic review
Domainnot available
GenreReview

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
Published2025
Admission routes1
Has abstractyes

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