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Record W2170371109 · doi:10.1123/jsr.9.4.329

The Use of Imagery by Athletes during Injury Rehabilitation

2000· article· en· W2170371109 on OpenAlexaboutno aff
Carla Sordoni, Craig Hall, Lorie A. Forwell

Bibliographic record

VenueJournal of Sport Rehabilitation · 2000
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsAthletesRehabilitationContext (archaeology)CognitionMental imagePsychologyPhysical medicine and rehabilitationPhysical therapyMotor imageryAthletic trainingMedicinePsychiatry

Abstract

fetched live from OpenAlex

Objectives: To determine whether athletes use motivational and cognitive imagery during injury rehabilitation and to develop an instrument for measuring imagery use. Design: A survey concerning imagery use during rehabilitation was administered to injured athletes. Setting: The Fowler Kennedy Sport Medicine Clinic in London, Ontario, Canada. Participants: Injured athletes (N = 71) receiving physiotherapy. Main Outcome Measure: The Athletic Injury Imagery Questionnaire (AIIQ). Results: As hypothesized, 2 distinct factors emerged from the items on the AIIQ: motivational and cognitive imagery. Motivational imagery was used more often than cognitive imagery in this context, yet less frequently than in other sport situations (eg, training and competition). Conclusions: The study indicates that the AIIQ is a potentially useful tool through which physiotherapists and sport psychologists can examine athletes' use of imagery in injury rehabilitation.

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)0.001
metaresearch head score (Gemma)0.007
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.295
Teacher spread0.284 · 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

Citations79
Published2000
Admission routes1
Has abstractyes

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