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Record W4386576343 · doi:10.1123/iscj.2022-0100

Exploring the Experiences of Community Sport Coaches: Stressors, Coping Strategies, and Mental Health

2023· article· en· W4386576343 on OpenAlexaffabout
Kelsey Hogan, Matthew Vierimaa, Lori Dithurbide

Bibliographic record

VenueInternational Sport Coaching Journal · 2023
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsAcadia UniversityDalhousie University
Fundersnot available
KeywordsMental healthStressorCoachingPsychologyThematic analysisCoping (psychology)Applied psychologyMental health literacyClinical psychologyQualitative researchPsychiatryMental illnessPsychotherapist

Abstract

fetched live from OpenAlex

In recent years, athlete mental health has received increased attention from researchers; however, coaches also experience stressors that can impact their mental health. This study addressed a gap in the sport coaching literature by using a phenomenological approach to explore the experiences of community sport coaches in Canada—an understudied population that makes up a large portion of the coaching workforce. Nineteen coaches from Atlantic Canada discussed stressors, coping strategies, and mental health in one-on-one semistructured interviews. Interviews were transcribed verbatim and analyzed using reflexive thematic analysis. Results are presented in three higher order themes: mental health culture in sport, influences on coach mental health, and coping strategies and supports. Our findings suggest that community coaches experience a variety of stressors (e.g., interpersonal, personal, organizational) similar to elite coaches, but that the origin of stressors may be different in the community sport context. The impact of stressors can be mitigated by coaches’ coping strategies, access to training and resources, and aspects of the role that support their mental health (e.g., rewarding work). Finally, these results suggest that training should address gaps in mental health literacy for coaches to support their own mental health needs as well as their athletes’ needs.

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)0.002
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 score0.136
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.149
GPT teacher head0.393
Teacher spread0.244 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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