Psychology of sport injury: Selected debates and contemporary issues
Bibliographic record
Abstract
Over the past 50 years, research on the psychology of sport injury has proliferated. Indicative of this growth, numerous edited books, literature reviews and consensus statements have been produced. This research has yielded important insights into psychological factors implicated in injury risk, rehabilitation and return to sport. Our aim in this paper is to examine the psychology of sport injury literature, spanning injury onset, rehabilitation and the return to sport following injury, and to critically examine three key debates and/or issues of contemporary relevance. Towards this end, we first discuss definitional, conceptual and theoretical issues. Second, we synthesize key empirical findings regarding psychological factors implicated in injury occurrence, rehabilitation and return to sport following injury. Third, we discuss three contemporary debates and/or challenges relevant to moving the psychology of sport injury research and clinical practice forward. In particular, we examine: 1. the issue of what rehabilitation adherence is and whether adherence to a structured rehabilitation program is beneficial for achieving rehabilitation (clinical, functional) and sport-specific outcomes; 2. challenges associated with assessment of psychological readiness to return to sport; and 3. whether injured athletes are honest when completing subjective, self-report measures. Finally, we advance various directions for future scholarship. In addressing these four areas, we hope to stimulate further research and debate within the psychology of sport injury.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".