Canadian Athletic Therapists' Association Education Task Force Consensus Statements
Bibliographic record
Abstract
Context: A published commentary from 2 of the current authors acted as a catalyst for raising some key issues that have arisen in athletic therapy education in Canada over the years. Objective: The purpose of this article is to report on the process followed to establish a number of consensus statements related to postsecondary athletic therapy education in Canada. The consensus statements should act as a future plan for entry-level athletic therapy education. Design: Content validation for consensus statements. Setting: Video-conference meetings at 7 Canadian postsecondary colleges/universities. Patients or Other Participants: Canadian Athletic Therapists' Association (CATA) program directors and CATA leaders from education, certification, and program accreditation committees. Main Outcome Measure(s): A Delphi method and modified Ebel procedure were used to gather opinions from participants about athletic therapy education. Results: We created 10 consensus statements, with a series of caveats that are presented in this article. All components received at least 80% consensus from the expert validation group. Conclusions: The final Education Task Force Report was created and content was validated by a group of experts in the topics associated with every consensus statement. The final report was presented to the CATA Board of Directors for adoption and implementation.
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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 | 0.183 | 0.341 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 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".