Immersive Clinical Education Experiences in Athletic Training: A Report From the NATA Professional Education Committee
Bibliographic record
Abstract
Context Immersive clinical education experiences (ImCEs) are a recent addition to the Commission on Accreditation of Athletic Training Education standards. As such, there is little information on how athletic training programs design and implement ImCEs into the curriculum. Objective The purpose of this study was to explore the structure of ImCEs among athletic training programs and practices relating to identifying and developing ImCEs. Design Cross-sectional study. Setting Web-based survey. Patients or Other Participants A total of 103 of 265 Coordinators of Clinical Education for Commission on Accreditation of Athletic Training Education-accredited professional programs participated (women = 69, men = 29, 4 = prefer not to disclose, 1 = unanswered). Main Outcome Measures Coordinators of Clinical Education provided information about their program, timing and length of ImCEs, and the settings used. Program practices for preceptor selection and development, curricular design for simultaneous didactic coursework, and resources available to students were also investigated. Results The average number of ImCEs was 1.9, with a length of 4 to 28 weeks. Most programs have the first ImCE in the second year and primarily rely on college/university and secondary school settings. Programs reporting ImCEs less than 4 weeks in length and those requiring synchronous coursework during clinical immersion are of concern. Conclusions Athletic training programs are integrating ImCEs in a variety of ways. There may be confusion as to best practices and Commission on Accreditation of Athletic Training Education requirements for ImCEs.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".