Utilizing Constellation Peer Mentoring to Promote Social Support and Destigmatization: A Practical Outline for Sport Psychology Consultants
Bibliographic record
Abstract
Although high-profile athletes have begun to discuss their experiences with mental ill-health, athletes continue to feel the need to protect themselves from stigmatization. Constellation peer mentoring, wherein less-experienced protégés meet regularly with several experienced mentors, can facilitate the formation of socially supportive teammate relationships and a reduction in the perceived barriers to help-seeking. These benefits help create an environment in which athletes feel comfortable reaching out to teammates to discuss the issues that they are experiencing (i.e., reduced stigma), protecting against mental ill-health symptoms or reducing their attendant negative effects. In the current article, we detail how practitioners can execute a constellation peer-mentoring program and adapt it to include a mental health literacy session to enhance the destigmatizing benefits experienced by athletes. Consequently, practitioners may be able to foster a positive mental health environment while providing the instrumental and psychosocial development benefits of peer mentoring.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".