An Innovative Doctor of Physical Therapy Experiential Learning Opportunity With Older Adults: A Description of a Unique Academic and Long-Term Care Partnership
Bibliographic record
Abstract
Background and Purpose. Despite the dramatic increase in older adults and the reality that this population often comprises a substantial proportion of physical therapists' client base, many Doctor of Physical Therapy (DPT) students feel unprepared or less willing to provide care to older adults after graduation. Method/Model Description and Evaluation. The University of Minnesota DPT program developed a unique model for all first-year DPT students to gain experiential learning with older adults through a year-long Clerkship experience. In addition to regular assessments of student learning, a pre/postmeasure of student attitudes toward older adults and a thematic analysis of student reflective journals were conducted. Outcomes. Students entered the DPT program with positive attitudes toward older adults. Although limited changes in student attitudes was observed over time through the quantitative pre/postmeasure, student reflective journals revealed more nuance, with many students describing gains in knowledge and confidence in practicing with older adults, as well as more positive attitudes toward older adults and geriatrics over time. Discussion and Conclusion. The University of Minnesota DPT first-year Clerkship experience is a unique experiential learning program that provides DPT students hands-on experience with older adults over an academic year in a real-life setting. This innovative approach dually contributes to the development of DPT students' essential competencies and has been shown to be beneficial to first-year DPT students in developing positive attitudes toward and comfort in working with older adults, thereby advancing toward a more prepared physical therapist workforce in the area of geriatrics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".