Mental health literacy practices within Australian football league next generation academy clubs: An exploratory study
Bibliographic record
Abstract
Purpose To explore and describe the extent and quality of Mental Health Literacy (MHL) resources, information and education currently available within the Australian Football League (AFL) Next Generation Academy (NGA) programs and identify limitations/gaps in existing MHL practices within these programs. Methods An exploratory mixed-method descriptive design was utilised in two phases. Phase One consisted of a researcher led MHL audit of publicly available data associated with each NGA program. Phase Two included a web-based open-ended questionnaire distributed to key NGA personnel and focused on mental health practices within the programs. Descriptive statistics were used to present Phase One data and thematic analysis was utilised in Phase Two. Results In Phase One, the total mean standardised score for resources on mental health, raising awareness on mental health and culture of support within the clubs were 47% (SD = 16, range 18–89), 51% (SD = 23, range 0–77) and 61% (SD = 27, range of 9–97). In Phase Two, eight participants responded to the questionnaire (44% response rate). Three themes emerged from the thematic analysis: 1) current initiatives and resources within the club; 2) training, education and support for staff; 3) the gender divide. Conclusion Mainstream mental health resources do exist within the AFL and there is some support within the professional league. However, these are not effectively tailored for youth elite athletes, nor have they been implemented into NGA programs despite the willingness of staff in supporting the mental health of these athletes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".