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Using Imagery to Enhance Three Types of Exercise Self‐Efficacy among Sedentary Women

2011· article· en· W1917583567 on OpenAlexaff
Lindsay R. Duncan, Wendy M. Rodgers, Craig Hall, Philip M. Wilson

Bibliographic record

VenueApplied Psychology Health and Well-Being · 2011
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsWestern UniversityBrock UniversityUniversity of Alberta
Fundersnot available
KeywordsPsychological interventionMultivariate analysis of varianceCoping (psychology)Analysis of varianceMultivariate analysisGuided imageryPsychologySelf-efficacyPhysical therapyMental imageRepeated measures designClinical psychologyCognitionMedicineAnxietySocial psychologyComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

The purpose of this study was to determine if task, coping, and scheduling self‐efficacy (SE) for exercise could be influenced using guided imagery interventions in an experimental design controlling for overt exercise experiences. Healthy women ( N = 205, M age = 31.47) who did not exercise regularly were randomly assigned to guided imagery sessions targeting a specific type of SE or to a nutrition information control group. All participants attended a 12‐week cardiovascular exercise program. The influence of the various imagery interventions on the three types of self‐efficacy over time were assessed with two doubly multivariate ANOVAs: one from baseline to 6 weeks and the other from 6 to 12 weeks. The analyses were conducted for 61 participants who completed the exercise intervention. The first analysis demonstrated that task, coping, and scheduling SE were differentially influenced over time in response to the different imagery interventions. The results of the second analysis were non‐significant, revealing that the main changes in SE were observed within the first half of the 12‐week intervention. This study demonstrates that task, coping, and scheduling SE can be seen as independent from one another and that mental imagery interventions are an effective means for influencing exercise‐related cognitions.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.756
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.045
GPT teacher head0.396
Teacher spread0.350 · 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

Citations24
Published2011
Admission routes1
Has abstractyes

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