Recommendations to promote mental health in dual career development environments: An integrated knowledge translation approach
Bibliographic record
Abstract
A dual career (DC) entails the combination of elite sport with a second career in education or work. Engaging in a DC has considerable short and long-term benefits for elite athletes. Nevertheless, evidence suggests that DC engagement can also be highly demanding and pose a challenge for athletes’ mental health. As such, dual career development environments (DCDEs), aimed to facilitate effective DC engagement, hold a responsibility to promote and safeguard athletes’ mental health within their organization. Given a lack of guidance within the literature to set up effective support systems, the purpose of our paper is to provide a comprehensive set of mental health recommendations for applied DCDEs. Adopting an integrated knowledge translation approach, the recommendations were developed based on a multiple case analysis of seven DCDEs, followed by a two-day working group with applied and academic experts within the fields of mental health and DC. In total, 12 key recommendations are provided across four overarching domains. These four domains include: organizational foundations, monitoring and follow-up, mental health literacy, and preventive well-being interventions.Lay summary: Organizations facilitating the combination of elite sport and study or work, hold a key responsibility to promote and safeguard the mental health of their athletes. In this study, we developed a mental health promotion framework consisting of four dimension and twelve recommendations to help organizations achieve this objective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".