Student adaptation to problem-based learning in an entry-level master's physical therapy program
Bibliographic record
Abstract
The purpose of this study was to describe physical therapy students' perceptions of how they were adapting to problem-based learning in an entry-level Master's program. Fifty-one students wrote weekly journal entries about their learning during their first academic unit. Three faculty members independently read and coded the entries and worked together to form categories and themes. Member checking was used to further verify the data interpretation. Tutorial sessions, evaluations, clinical experiences, and accessing resources were the most frequently mentioned learning events. The students were initially overwhelmed by the program demands, but quickly developed strategies to deal with each new challenge. In the process, they gained confidence and, by the end of the unit, acknowledged their significant accomplishments. The themes associated with the adaptation to problem-based learning included the students' need to: establish their own learning structure, learn more efficient and effective means of accessing information, develop ways of coping with stress, and receive confirmation of their learning. Additional themes were: students' awareness of group dynamics, the difficulty and value of giving and receiving feedback, and the value of the educational process to both their learning and to the practice of physical therapy.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".