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Record W3092383644 · doi:10.1177/0308022620949093

Effects of a pain management programme on occupational performance are influenced by gains in self-efficacy

2020· article· en· W3092383644 on OpenAlexaboutno aff
Fiona Thomas, Stephen J. Gibson, Carolyn Arnold, Melita J. Giummarra

Bibliographic record

VenueBritish Journal of Occupational Therapy · 2020
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsOccupational therapySelf-efficacyConfidence intervalMedicinePhysical therapyActivities of daily livingCognitionPsychologyPhysical medicine and rehabilitationClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Introduction The perceived capacity to perform particular activities or skills (i.e. self-efficacy) is paramount in occupational therapy and is thought to be reinforced by actual functional capacity. This study examined whether changes in self-efficacy or confidence to lift weighted items influences changes in occupational performance and disability levels in patients attending a cognitive behavioural therapy pain management programme. Method Clients attending an 8-week cognitive behavioural therapy pain management programme ( N = 125) completed questionnaires before treatment, at discharge, and at 3-month and 6-month reviews, including measures of pain self-efficacy, disability and self-perceived performance and satisfaction using the Canadian occupational performance measure. Analyses examined disability and occupational performance over time, adjusting for baseline characteristics (age, sex, education), and sought to determine whether self-efficacy or lifting confidence influenced the outcomes. Results The level of disability, lifting confidence, self-efficacy and occupational performance all improved over time; however, only occupational performance and lifting confidence maintained improvements up to the 6-month review. Self-efficacy had a greater impact on occupational performance than lifting confidence.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.581
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.027
GPT teacher head0.313
Teacher spread0.286 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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