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Self‐efficacy, imagery use, and adherence during injury rehabilitation

2011· article· en· W1790691278 on OpenAlexaff
Natascha N. Wesch, Craig Hall, Harry Prapavessis, Ralph Maddison, Sandra Bassett, Louise Foley, Stephanie Brooks, Lorie A. Forwell

Bibliographic record

VenueScandinavian Journal of Medicine and Science in Sports · 2011
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineRehabilitationPhysical medicine and rehabilitationPhysical therapy

Abstract

fetched live from OpenAlex

Previous observational studies examining imagery, self-efficacy, and adherence during injury rehabilitation have been cross-sectional and thus have not provided a clear representation of what occurs over the course of the rehabilitation period. The objectives of this research were (1) to examine the temporal patterns of imagery, self-efficacy, and rehabilitation adherence during an 8-week rehabilitation program and (2) to identify the time-order relationships between imagery, self-efficacy, and adherence. The design of the study was prospective and observational. 90 injured people (n=57 males; n=33 females) aged 18-78 years attending an injury rehabilitation clinic participated. The main outcome measures were imagery (cognitive, motivational, and healing), self-efficacy (task and coping), and rehabilitation adherence (duration, quality, and frequency). Results indicated that task efficacy, imagery use, and adherence levels remained stable, while coping efficacy declined over time. During the course of rehabilitation, moderate to strong reciprocal relationships existed between self-efficacy and adherence to rehabilitation. Weak to moderate relationships were found between imagery use and rehabilitation adherence. The results of this study can be used to inform the development of interventions steeped in self-efficacy and imagery aimed at improving rehabilitation adherence and treatment outcome.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.040
GPT teacher head0.337
Teacher spread0.297 · 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

Citations34
Published2011
Admission routes1
Has abstractyes

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