The Future of RT/TR Education: Results from the ATRA Higher Education Task Force Study
Bibliographic record
Abstract
In 2016, the American Therapeutic Recreation Association (ATRA) Board of Directors created a task force within its Higher Education Committee to study the educational requirements for entry-level education in recreational therapy/therapeutic recreation (RT/TR) and make recommendations to the Board. From 2016-2018, the task force planned and implemented a multiphase mixed methods study with approximately 2,000 RT/ TR practitioners, educators, students, and credentialing and accrediting bodies from across the United States and Canada. During the first phase of the study, in-person focus groups were completed with 25 practitioner groups (N=257), 10 educator groups (N=49), and 17 student groups (N=222) at 19 state and regional conferences and meetings, as well as during four online focus groups using the Zoom videoconferencing platform. Interviews were conducted with board members of six RT/TR credentialing and accrediting bodies. During the second phase of the study, online surveys were completed by RT/TR practitioners (N=1,663), educators (N=141), and students (N=483). The central finding suggests the most current and pressing need in higher education is to improve the quality and consistency of the bachelor’s degree in RT/TR. Five mixed-method results supporting the central finding are presented, and data-driven recommendations to improve professional preparation in RT/TR are discussed.
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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 | 0.029 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".