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Record W4416222025 · doi:10.1177/15394492251388033

Health Behavior Performance After a Personalized Occupational Therapy Intervention in Cancer Survivors

2025· article· en· W4416222025 on OpenAlexaboutno aff
Alix G. Sleight, Yoko E. Fukumura, Sandy C. Takata, Alexandra E. Feldman, Pamela Roberts, Kim Bissell, L J Amaral, Kathleen Doyle Lyons

Bibliographic record

VenueOTJR Occupational Therapy Journal of Research · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
FundersWorld Cancer Research FundAmerican Occupational Therapy FoundationAmerican Society of Preventive OncologyAmerican Association for Cancer Research
KeywordsIntervention (counseling)CancerOccupational therapyHealth behaviorOccupational safety and healthCancer treatmentPublic health

Abstract

fetched live from OpenAlex

BACKGROUND: Health behaviors significantly influence health outcomes after cancer. However, few studies have tested occupational therapy (OT) self-management training to catalyze health behavior change. OBJECTIVES: To establish proof of concept of a 12-week OT intervention designed to improve occupational performance and/or satisfaction in cancer survivors. METHODS: This single-arm, prospective study used the Canadian Occupational Performance Measure to measure change in occupational performance and satisfaction related to health behaviors in a convenience sample of 20 cancer survivors. RESULTS: = 20) post-intervention. A total of 18 participants (86%) demonstrated a clinically significant change in performance scores (≥2), and 19 participants (95%) demonstrated a clinically significant change in satisfaction scores (≥2). CONCLUSION: OT, when leveraged for a health self-management intervention, may result in improvements in both occupational performance and satisfaction related to health behavior in cancer survivors.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.151
GPT teacher head0.503
Teacher spread0.352 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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Same venueOTJR Occupational Therapy Journal of ResearchSame topicCancer survivorship and careFrench-language works237,207