The Intra-rater Reliability of Nine Content-Validated Technical Skill Assessment Instruments (TSAI) for Athletic Taping Skills.
Bibliographic record
Abstract
Objective: To establish the intra-rater reliability of nine content-validated Technical Skill Assessment Instruments (TSAI) for the skills of athletic taping. Setting: University of Calgary. Subjects: Canadian Certified Athletic Therapists, CAT(C), with a mean ± SD of 9.6 ± 10.8 years as a CAT(C), 7.8 ± 10.9 years as a Supervisory Athletic Therapist, 8.5 ± 12.0 years teaching athletic taping skills, and 9.2 ± 11.5 years evaluating athletic taping skills. Design: Six Certified Athletic Therapists from Canada completed the repetitive evaluations of nine different athletic taping scenarios. Each rater evaluated the performance of a student therapist taping a standardized patient while using the appropriate TSAI designed for each athletic taping scenario. Evaluations occurred once per month for five successive months. Raters viewed the performances on a portable DVD player at a central testing site. Measurements: The percent scores of 270 completed TSAIs were used for analysis. ICC (3, k) was used to quantify the intra-rater reliability. We used a One-way ANOVA with repeated measures to determine if mean differences across testing months existed within raters. Significance was achieved with α = 0.05. Results: ICC values for the nine TSAIs ranged from 0.65 to 0.95 with Ankle 3 and Thumb 2 achieving the lowest and highest ICC values respectively. One-way ANOVA with repeated measures did not provide significant mean differences between testing months within each rater. Conclusion: The nine TSAIs possess substantial to almost perfect reliability with seven TSAIs appropriate for summative evaluations and two appropriate for formative evaluations.
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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 | 0.023 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".