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The Effects of a Motivational General-Mastery Imagery Intervention on the Sport Confidence of High-Level Badminton Players

2001· article· en· W1975487503 on OpenAlexaff
Nichola Callow, Lew Hardy, Craig Hall

Bibliographic record

VenueResearch Quarterly for Exercise and Sport · 2001
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsWestern University
Fundersnot available
KeywordsPsychologyConfidence intervalPsychological interventionIntervention (counseling)Sport psychologyMultiple baseline designMental imagePhysical therapyApplied psychologyMedicineCognition

Abstract

fetched live from OpenAlex

A multiple-baseline across-participants design was used to examine the effects of a Motivational General-Mastery imagery intervention on the sport confidence of 4 high-leveljunior badminton players. Sport confidence data were collected once a week for 21 weeks prior to international and county matches. The imagery intervention consisted of six imagery sessions (two per week for 3 weeks) and was administered using a multiple-baseline design with interventions commencing at Weeks 5, 7, 9, and 11 for Participants 1, 2, 3, and 4, respectively. Results of visual inspection and Binomial tests suggested significant increases in sport confidence for Participants 1 and 2, a significant decrease in sport confidence for Participant 3, and a delayed increase in sport confidence for Participant 4. The results are discussed in terms of the implications of using mastery imagery and the usefulness of multiple-baseline designs for furthering imagery research.

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)0.002
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0000.001
Research integrity0.0000.001
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 head0.053
GPT teacher head0.376
Teacher spread0.324 · 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

Citations195
Published2001
Admission routes1
Has abstractyes

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