Bibliographic record
Abstract
Coaches’ experiences of burnout and stress have been popular topics for research within sport psychology, particularly over the last decade. The purpose of this scoping review was to provide an up-to-date and critical review of the coaching burnout literature, consolidate research findings, assess current methodological and conceptual trends, and identify avenues for research in this area. Five electronic databases were used to conduct the literature search up to September 30th, 2017 (PsycINFO, Web of Science, PubMed, SPORTDiscus, ORIA, Google Scholar). Initially, 65 papers, reviews, and books chapter were identified, but through an iterative process, 45 peer-reviewed, published articles satisfied the inclusion criteria, and the data from these studies was charted. Findings indicated that coach burnout literature is explored from a number of different theoretical perspectives, and shortcomings were identified regarding constructs and concepts used, and research quality. Based on consolidated findings, key challenges are identified, and recommendations for future research are suggested. Recommendations include the use of designs that fully capture the enduring nature of the burnout experience, further consideration being given to the measurement of coach burnout, and further research exploring the clinical treatment and prevention of burnout in coaching contexts.
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.
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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.025 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".