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Record W4317425058 · doi:10.1123/apaq.2022-0002

Coach and Athlete Perspectives on Talent Transfer in Paralympic Sport

2023· article· en· W4317425058 on OpenAlexaffabout
Nima Dehghansai, Alia Mazhar, Joseph Baker

Bibliographic record

VenueAdapted Physical Activity Quarterly · 2023
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsYork University
Fundersnot available
KeywordsAthletesPsychologyApplied psychologySocial psychologyPhysical therapyMedicine

Abstract

fetched live from OpenAlex

Research pertaining to the experiences and motives of Paralympic athletes who transfer between sports is scant. This study aimed to address this gap through semistructured interviews with Canadian Paralympic coaches (n = 35) and athletes (n = 12). Three higher-order themes of "alternative to retirement," "career extension," and "compatibility" were identified. The subthemes of "psychobehavioral" and "physical and physiological" (from the higher-order theme of alternative to retirement) captured reasons leading to transfer, which are similar to reasons athletes may consider retirement. The subthemes of career extension-"better opportunities" and "beneficial outcomes"-shed light on factors that contributed to the withdrawal of negative experiences and reinforcement of positive outcomes associated with transferring sports. Last, compatibility had three subthemes of "resources," "sport-specific," and "communication," which encapsulated factors athletes should consider prior to their transfer. In conclusion, the participants highlighted the importance of transparent and effective communication between athletes and sports to align and establish realistic expectations for everyone involved.

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)0.000
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 score0.849
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.001

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 head0.318
Teacher spread0.291 · 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

Citations8
Published2023
Admission routes2
Has abstractyes

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