Teammate support for injured athletes: Assessment and intervention considerations
Bibliographic record
Abstract
Teammates trained to provide effective support may be well-positioned to help athletes cope with the experience of injury. However, researchers have not developed a scale that measures teammate support specific to the task of rehabilitation from injury and have yet to implement a support intervention involving teammates. Therefore, the aims of the present investigation were two-fold. The first purpose was to develop and assess the psychometric properties of a Teammate Support Following Injury (TSFI) scale. Five experts in the area of psychology of injury and five intercollegiate athletes reviewed the TSFI to establish face validity. Modifications to the initial TSFI resulted in a 26-item scale reflecting five dimensions of rehabilitation-specific social support including instrumental, informational, emotional, motivational, and athletic identity support. Then, 112 (63 male and 49 female) intercollegiate and competitive athletes who had incurred an injury that prevented them from participating in sport/exercise activity for the period of at least one week were asked to complete the TSFI. They also completed measures of emotional response, athletic identity, and perceived support to assess the content validity of the TSFI, and a demographic questionnaire. A principle axis factor analysis with oblique rotation performed on the data resulted in three factors designated as athletic identity, instrumental/rehabilitation, and emotional support. The three subscales of the TSFI had excellent internal reliability and acceptable levels of concurrent validity with a structural support measure. The second purpose was to gain a better understanding of the responses to the TSFI and to assess participants' experiences of support and preferences for a support intervention involving teammates. Six participants who scored in the upper and lower quartiles on the TSFI participated in a personal interview. A directed content analysis revealed that injured athletes may not need practical support from teammates and do not expect teammates to provide information during their recovery. The results also indicated various social processes, contextual factors, potential mediating mechanisms, and outcomes of support important to structuring support interventions. These findings are discussed and recommendations are made for future support interventions that involve teammates.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".