Can Preinjury Adversity Affect Postinjury Responses? A 5-Year Prospective, Multi-Study Analysis
Bibliographic record
Abstract
Informed by and drawing on both the integrated model of response to sport injury (Wiese-Bjornstal, Smith, Shaffer, & Morrey, 1998) and the biopsychosocial model of challenge and threat states (Blascovich, 2008), this multi-study paper examined whether preinjury adversity affected postinjury responses over a five-year time period. Study 1 employed a prospective, repeated-measures methodological design. Non-injured participants (N=846) from multiple-sites and sports completed a measure of adversity (Petrie, 1992); 143 subsequently became injured and completed a measure of coping (Carver, Scheier, & Weintraub, 1989) and psychological responses (Evans, Hardy, Mitchell, & Rees, 2008) at injury onset, rehabilitation, and return to sport. MANOVAs identified significant differences between groups categorized as low, moderate, and high preinjury adversity at each time phase. Specifically, in contrast to low or high preinjury adversity groups, injured athletes with moderate preinjury adversity experienced less negative psychological responses and used more problem-and emotion-focused coping strategies. Study 2 aimed to provide an in-depth understanding of why groups differed in their responses over time, and how preinjury adversity affected these responses. A purposeful sample of injured athletes from each of the three groups were identified and interviewed (N=18). Using thematic analysis, nine themes were identified that illustrated that injured athletes with moderate preinjury adversity responded more positively to injury over time in comparison to other groups. Those with high preinjury adversities were excessively overwhelmed to the point that they were unable to cope with injury, while those with low preinjury adversities had not developed the coping abilities and resources needed to cope postinjury. Practical implications and future research directions are discussed.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".