Exploration of the mental health perceptions and experiences of current elite soccer players in India
Bibliographic record
Abstract
Background. India as a country lags behind in supporting the mental health of athletes. Discussing or talking about one’s mental health has long been a struggle in the Indian sporting context. There is a gap in the literature looking at the experiences of elite athletes with mental health. Aim. The aim of this research was to develop an understanding of how mental health is perceived by elite football (soccer) athletes in India. This study was an exploratory study looking at the perceptions and experiences of mental health of the elite soccer players in India to map the current situation eventually leading to social change. Two continua model of mental health was used as a guiding theoretical framework. The research question this addressed was: what are the perceptions about and experiences with mental health of the professional soccer players in India? Methods. Semi-structured interviews with open ended questions were used to interview six professional soccer athletes. An inductive thematic analysis was used to analyse the interview transcripts. Results. Three themes emerged from the transcripts: a) mental health, b) being an athlete is not easy, and c) a way forward. These findings highlight the perceptions, the mental states, and the experiences with mental health of the elite athletes. Implications. This research can help the coaches, sport federations, sport psychologists, athletic therapists, educators and policy makers better understand the lived experiences of elite athletes with regards to mental health in India. Keywords: mental health, elite athletes, two-continua model of mental health, football (soccer)
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".