MétaCan
Menu
Back to cohort
Record W2894094358 · doi:10.1123/jcsp.2018-0052

The Level of Mental Health Literacy Among Athletic Staff in Intercollegiate Sport

2018· article· en· W2894094358 on OpenAlexaff
P.F. Sullivan, Jessica Murphy, Mishka Blacker

Bibliographic record

VenueJournal of Clinical Sport Psychology · 2018
Typearticle
Languageen
FieldHealth Professions
TopicAthletic Training and Education
Canadian institutionsBrock University
Fundersnot available
KeywordsMental health literacyMental healthPsychologyAthletesContext (archaeology)Affect (linguistics)PopulationClinical psychologyHealth literacyPsychiatryMedicinePhysical therapyHealth careMental illnessEnvironmental health

Abstract

fetched live from OpenAlex

Mental health literacy (MHL), the knowledge and attitudes that aid in recognition, management and prevention of mental health issues, could help maintain positive mental health within the athletic community. As coaches and athletic therapists (ATs) frequently and routinely interact with athletes, this study focused on the MHL of these individuals. Eighty participants (24 females, 54 males; 57 coaches, 18 ATs) completed an on-line version of the MHL Scale. Average MHL score was 131.48, which, is relatively equal to scores seen in university students and a general population. No significant difference was detected between coaches and ATs but females reported significantly higher MHL scores than males. There was a significant negative correlation between MHL and total experience. These results have potentially strong clinical ramifications as increased MHL in this context can affect facilitators and barriers towards seeking help in a high-risk population.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.311
GPT teacher head0.616
Teacher spread0.305 · 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 source (direct Gemma or distilled Codex), 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

Citations35
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical Sport PsychologySame topicAthletic Training and EducationFrench-language works237,207