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Record W2168935939 · doi:10.1177/000841740807500203

Evaluating ADL Measures from an Occupational Therapy Perspective

2008· article· en· W2168935939 on OpenAlexaffvenue
Sheryl Klein, Ingrid G Barlow, Vivien Hollis

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2008
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsGlenrose Rehabilitation Hospital
Fundersnot available
KeywordsOccupational therapyActivities of daily livingConceptualizationPsychologyPerspective (graphical)MedicinePhysical therapyClinical psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Measures reflecting occupational therapy's conceptualization of occupational performance support the profession's contribution to evidence-based practice and fiscal accountability. PURPOSE: This study compared measures of performance-based activities of daily living (ADL) with principles of occupational therapy practice and intended outcomes. METHODS: Using an action research study design, occupational therapists and researchers (N= 13) systematically clarified the clinical problem, identified occupational therapy principles inherent to the assessment of daily living activity via nominal group technique; defined the key principles as constructs; and reframed these constructs as a questionnaire against which 18 published standardized ADL measures were evaluated. FINDINGS: Participants identified six measures as most congruent with principles of occupational therapy practice: ADL Profile, Assessment of Motor and Process Skills, Functional Performance Measure, Rivermead ADL Assessment, Edmans ADL Index, and Melville-Nelson Self-Care Assessment. IMPLICATIONS: Findings guide occupational therapists' search and use of performance-based ADL measures that demonstrate the profession's distinct health care contribution.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science 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 score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.687
GPT teacher head0.606
Teacher spread0.081 · 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
Published2008
Admission routes2
Has abstractyes

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