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Record W3216487794 · doi:10.1123/jcsp.2021-0011

A Content Analysis of Mental Health Literacy Education for Sport Coaches

2021· article· en· W3216487794 on OpenAlexaff
Stephen P. Hebard, James E. Bissett, Emily Kroshus, Emily Beamon, Aviry L. Reich

Bibliographic record

VenueJournal of Clinical Sport Psychology · 2021
Typearticle
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMental health literacyMental healthPsychologyPromotion (chess)LiteracyContent analysisHealth promotionAthletesApplied psychologySample (material)Health literacyMedical educationHealth educationPedagogyPublic healthNursingMedicinePsychiatryHealth careSocial scienceSociologyMental illnessPolitical sciencePolitics

Abstract

fetched live from OpenAlex

Sport coaches can play an influential role in athletes’ mental health help seeking through purposeful communication, destigmatization of mental health concerns, and supportive relationships. To positively engage in these behaviors, coaches require mental health knowledge (or literacy), positive attitudes about that knowledge, and self-efficacy to use that knowledge. Guided by a multidimensional health literacy framework, we conducted a content analysis of web content and scholarly literature to identify health education programming for coaches that addressed athlete mental health. A purposive sample of Olympic National Governing Bodies, collegiate athletic associations, high school sport associations, youth sport governing bodies, and the scholarly literature were analyzed. We found inconsistent programming regarding a range of mental health disorders, behaviors critical to mental health promotion, and critical components of mental health literacy. Implications and next steps for mental health literacy support for coaches are discussed.

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)0.005
metaresearch head score (Gemma)0.001
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 score0.091
Threshold uncertainty score0.901

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.370
GPT teacher head0.663
Teacher spread0.293 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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