Perfectionism and rehabilitation overadherence among injured athletes
Bibliographic record
Abstract
Rehabilitation overadherence is a form of nonadherence in which individuals exceed practitioners' guidelines regarding the rehabilitation of their injury resulting in enhanced risk for re-injury and prolonged recovery (Granquist et al., 2014). Overadherence is common among overly-motivated injured athletes with intense personalities (Niven, 2007). This suggests that perfectionism may be a meaningful predictor of rehabilitation overadherence among injured athletes. This study utilized the 2 A— 2 model of perfectionism (Gaudreau & Thomspon, 2010) to investigate this claim. Injured athletes (N = 82; Mage = 27.45 years, SD = 10.88) currently undergoing supervised rehabilitation completed measures of two perfectionism dimensions (personal standards and evaluative concerns) and four overadherence risk-factors (effortful healing, expedited rehabilitation, inclinations to overadhere, and normalization of pain). Multiple regression tested whether the perfectionism dimensions interacted to predict each overadherence risk-factor. No significant effects were found for effortful healing and expedited rehabilitation. A significant main effect (B = 0.17) indicated that higher levels of evaluative concerns predicted greater inclinations to overadhere. A significant interaction effect (B = -0.07) identified a similar relationship between evaluative concerns and normalization of pain, but specified that this relationship was greatest when personal standards were low. Findings are interpreted in-line with the 2 A— 2 model's hypotheses and identified as initial evidence of associations between perfectionism and sport injury rehabilitation overadherence. In discussion, we speculate as to why relationships were evident for some overadherence risk-factors, but not others, elaborate on the role of evaluative concerns perfectionism in overadherence, and suggest practical implications for practitioners.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".