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Record W4404104178 · doi:10.1123/iscj.2023-0126

Exploring How the COVID-19 Pandemic Impacted the Coach–Athlete Relationship for Travel Sport Coaches: A Qualitative Study

2024· article· en· W4404104178 on OpenAlexaboutno aff
Keith McShan, E. Whitney G. Moore

Bibliographic record

VenueInternational Sport Coaching Journal · 2024
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakAthletesQualitative researchSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PsychologySociologyMedicinePhysical therapyVirologySocial science

Abstract

fetched live from OpenAlex

During the COVID-19 pandemic, maintaining the quality of the coach–athlete relationship (CAR) became a significant challenge for travel sport coaches. The first aim of this study was to explore the coaches’ perceptions of how the CAR’s characteristics (i.e., closeness, commitment, and complementarity) were affected by the COVID-19 pandemic. The second aim was to explore the differences before and during the COVID-19 pandemic in CAR quality from the coaches’ perspective. Fourteen travel/club coaches from Ontario took part in 90-min semistructured interviews. Generally, coaches believed that their closeness was maintained, commitment levels improved, and complementarity decreased within their CAR. Past relationships between the coach and athletes helped to maintain their closeness. The resiliency of athletes was thought to aid in the increased commitment. Lack of face-to-face interactions hampered complementarity. Three themes—barriers, variability, and benefits—emerged as differences within the CAR during the pandemic. Recommendations from this study are that coaches focus on the characteristic of complementarity to enhance their CAR quality following the COVID-19 pandemic.

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)0.008
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 score0.216
Threshold uncertainty score0.839

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.420
GPT teacher head0.477
Teacher spread0.056 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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