Athletic Trainers' Exposure to Telemedicine Influences Perspectives and Intention to Use
Bibliographic record
Abstract
Purpose: To examine athletic trainers' perspectives of telemedicine compared between self-identified users and non-users. Methods:A cross-sectional web-based survey delivered to credentialed athletic trainers who were members of the National Athletic Trainers' Association was used.The survey included demographic information and whether participants self-identified as users or non-users of telemedicine based on a provided definition.The telemedicine tool had 39 items adopted from previous literature and adapted for athletic training.The tool examined six subscales: perceived advantages, perceived disadvantages, current knowledge, perceived necessity, perceived security, and perceived ease-of-use.Results: Participants who self-identified as users of telemedicine had higher agreement about advantages, stronger disagreement about disadvantages, and higher knowledge, and saw greater perceived necessity for use in practice. Conclusions:The results suggest athletic trainers with exposure to telemedicine have more positive perceptions respective to efficiency, knowledge, necessity, benefits, and drawbacks.The authors suggest athletic trainers have formal exposure in professional, post-professional, and continuing education.
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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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".