Student Perceptions of the Athletic Therapy Interactive Concussion Educational (AT-ICE) Tool
Bibliographic record
Abstract
Previous research has identified a considerable amount of variability in how healthcare professionals are taught to recognize, assess, and manage concussions. Responding to these findings, an innovative applied learning technology tool, the Athletic Therapy Interactive Concussion Educational (AT-ICE) Tool, was developed to help teach athletic therapy students how to recognize, assess, and manage concussions. The purpose of this research was to employ an interpretivist conceptual framework to explore athletic therapy students’ perceptions of this tool. A questionnaire was used to identify individual factors that impacted student perceptions of AT-ICE and how it could be integrated into the classroom. Overall, participants enjoyed using AT-ICE and felt it helped to stimulate their critical thinking about the entire continuum of concussion care. Several important themes emerged including the importance of detailed scenarios, sharing lived experiences, and integrating anatomy within assessment and management scenarios. Findings suggest that AT-ICE was an effective educational technology that stimulated critical thought throughout the entire continuum of concussion care. Future research could continue to investigate the effectiveness of the tool or explore different ways to implement it in formal athletic therapy educational settings.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".