Teaching Interpersonal Communication Skills in Athletic Training Professional Education: A Mixed Methods Study.
Bibliographic record
Abstract
Studies examining the impact interpersonal communication skills have on patient satisfaction, outcomes, and patient compliance have been conducted in healthcare.In addition, athletic training research suggests communication is a top attribute observed when hiring, yet many athletic trainers are deficient in their ability to communicate effectively.Although communication skills are highly important in athletic training, little research exists on how to teach such skills in athletic training programs.This was a mixed methods treatment randomized baseline post-test control group study designed to determine the effectiveness of a six-week communication skills training on athletic training students' interpersonal communication skills during initial patient encounters, whether athletic training students utilize effective interpersonal communication in the athletic training clinical education setting, and to understand athletic training students' perceptions of their interpersonal communication skills.Data were collected from 8 (n=8) athletic training students enrolled in an athletic training professional program during fall 2018 using a modified Calgary-Cambridge Observation Guide-Medical Skills Evaluation during patient encounters with a standardized patient.Results indicated athletic training students improved their communication skills over time by a mean score of 10 points out of 120 points once taught communication skills.Students perceived their communication skills to improve by a mean of 24.38 points out of 120 points, which research suggests this may be due to the student being less confident in a skill.Athletic training students' communication scores improved by a mean score of 23.75 out of 120 points when provided an opportunity to apply the skills learned in clinical practice.iv v DEDICATION I dedicate this dissertation to my family.My husband, Joe Wehrlin supported me throughout the entire dissertation process and doctoral program.His words of encouragement gave me strength and motivated me to persevere through the end.A special feeling of gratitude goes to the best son a mother could ask for during doctoral work.Seth Wehrlin, I appreciate your patience and understanding over the years while I focused on my studies.My love is endless for both of you and I could not have completed this journey without either of you.vi
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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 | 0.019 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".