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Record W4313447404 · doi:10.1123/iscj.2022-0025

Examining the Impact of COVID-19 on Sport Coaches

2022· article· en· W4313447404 on OpenAlexaffabout
Anthony Battaglia, Gretchen Kerr

Bibliographic record

VenueInternational Sport Coaching Journal · 2022
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCoachingPandemicAthletesThematic analysisPsychologyCoronavirus disease 2019 (COVID-19)Applied psychologyPopulationMedical educationQualitative researchMedicineSociologyEnvironmental healthPsychotherapistPhysical therapyDisease

Abstract

fetched live from OpenAlex

Researchers have examined the impact of the COVID-19 pandemic on athletes’ experiences, however, there remains a lack of attention examining the impact of the pandemic on coaches’ experiences. Therefore, the purpose of this study was to examine Ontario sport coaches’ perspectives on the implications of the pandemic on their experiences. As part of a large-scale survey of Ontario coaches’ experiences in sport, an open-ended question was asked regarding the implications of COVID-19 on the coaching population. In total, 591 participant responses were analyzed using thematic analysis. According to participants, most of the cited outcomes of COVID-19 were negative, although some positive aspects were cited. Negative outcomes of the pandemic included adapting coaching methods and practices, insufficient coach supports, declines in coaching confidence and skills, lack of meaningful interpersonal connections, mental health concerns, job and financial instability, unclear guidelines on safe returns to sport, and loss of athletes and athletic programs. Conversely, positive impacts included having time to reflect on their coaching pursuits and alternative interests and to engage in professional development. These findings highlight the importance of understanding coaches’ experiences during the pandemic and may be used to inform recommendations for supporting coaches post 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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0130.000

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 head0.080
GPT teacher head0.401
Teacher spread0.321 · 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

Citations8
Published2022
Admission routes2
Has abstractyes

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