Progress and Prospects of Research on the Impact of Mental Health of Youth Sailors—A Bibliometric-Based Analysis
Bibliographic record
Abstract
Background: The mental health of youth sailors has garnered increasing attention from both coaches and researchers, as evidenced by the growing appearance of related keywords in scientific literature. Despite this rising interest, no studies have yet specifically focused on the mental health of this population. Methods: This study conducted a bibliometric analysis of 315 articles retrieved from the Web of Science database. These articles were analyzed to identify trends, influential authors, institutions, and regions in the field of youth sailor mental health. Results: The analysis yielded several key findings: (1) Depression, anxiety, and mental health disorders are the primary areas of focus in the literature on youth sailors’ mental health; (2) Rosemary Purcell is identified as the most influential author in this domain; (3) the University of Melbourne, Orygen, and Deakin University are the top three contributing institutions; (4) Australia, the USA, Canada, China, and England are the five most prominent regions involved in this research. Conclusions: This study provides a comprehensive overview of current research on the mental health of young sailors. By emphasising the most influential contributors and research trends, it aims to raise awareness amongst coaches and researchers, eventually supporting efforts to improve the mental health of young sailors.
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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 | 0.019 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.136 | 0.150 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".