Impact of COVID-19 on Athlete Mental Health – Strategies to Promote Emotional Wellness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recently, I have been heavily involved in discussions with my international colleagues and research teams from New Zealand, Europe, Canada, and United States, around identifying key strategies, solutions and protocols for elite basketball organizations following the unprecedented circumstances and challenges presented by Coronavirus disease (COVID-19).1-3 Our discussions have centred specifically on two key areas of player health and wellbeing, those being: (1) the potential mental health challenges faced by coaches, players and support staff,4-6 due to quarantine and isolation7demands of the unique, yet unknown “NBA bubble”;8,9 and (2) the overall impact of COVID-19 and the NBA bubble on player health and wellbeing, in the context of ‘emotional wellness’.6,10,11
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 it