Empathy in Action: Collegiate Athletic Trainers’ Approaches to Delivering Bad News and Setting Patient Goals
Bibliographic record
Abstract
Context Empathy involves understanding another person’s feelings, thoughts, and experiences and can improve patient outcomes and adherence to treatment recommendations. For athletes, unexpected time away from sport can be devastating, making the delivery of bad news by athletic trainers (ATs) crucial. After delivering bad news, setting patient goals is essential for optimal clinical outcomes. In this study, we explored the role of empathy in delivering bad news and patient goal setting by collegiate ATs. Design Nonexperimental, mixed-methods observational study. Methods Ninety-six collegiate ATs (age = 35 ± 11 years) participated. They completed the Toronto Empathy Questionnaire (0–64, higher scores indicate more empathy), the communicating bad news instrument (25–75, higher scores indicate better performance), and answered questions about their experiences in delivering bad news and with patient goal setting. Participants completed an applied, open-ended response for delivering bad news and simulated medical documentation, which were analyzed using the SPIKES protocol and a chart audit rubric. Descriptive statistics were calculated, and Mann-Whitney U tests compared empathy groups with goal setting and chart audit results. Pearson correlation assessed the relationship between perceived and actual goal-setting behaviors. Results Collegiate ATs’ average self-reported scores met or exceeded previously published empathy levels. All ATs had delivered bad news, but only 22.9% received formal training. The average score for the delivering-bad-news tool was 50%. Participants included 43% of the goal-setting criteria in their responses. Physical goals were included by 85.5% of ATs, but lifestyle goals were included by 28.1%. Conclusion Despite feeling knowledgeable and confident in delivering bad news and setting goals, a gap exists between perceived and actual practices. Despite high comfort levels and the use of structured formats like SMART goals, many ATs do not consistently set measurable goals, highlighting the need for improved training on goal setting.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".