Development of Content-Valid Technical Skill Assessment Instruments for Athletic Taping Skills
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The content validity of technical skill assessment instruments (TSAI) for the skills of athletic taping has not been reported. The purpose of this paper is to outline and present the process of content validation for nine TSAIs for athletic taping. Local and national validators were selected from Canadian Athletic Therapists' Association (CATA)-accredited athletic therapy (AT) programs to serve as content validators. METHODS: The process of content validation began with the creation of a detailed task analysis via mail and simple validation by local validators. Subsequently, the detailed task analysis was committee validated by a group of 10 validators from across Canada. Validators judged the importance and difficulty of each item, and a face-to-face committee-validator meeting established consensus on the majority of checklist items. Through a modified Ebel procedure, frequency distribution was used in the formation of the final TSAIs. RESULTS: Initial consensus for pre-taping assessment and technical skill performance items was low. Upon committee discussion and lack of agreement, the decision to remove pretaping assessment items was made. Initial results of importance and difficulty for athletic taping technical skills were low prior to the committee meeting. Results of importance and difficulty improved substantially following the face-to-face committee-validators meeting. Consensus on fail points improved from initial to final committee validation. CONCLUSION: The process of simple and committee validation can be seen as effective methods to establish the content validity of instruments used for the evaluation of athletic taping.
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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 | 0.064 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".