Evaluation of virtual problem-based tutorials in healthcare professional education
Bibliographic record
Abstract
Purpose To explore student and tutor perspectives on the learning efficacy of virtual, compared to in-person, problem-based tutorial (PBT) in occupational therapy, physiotherapy, and speech-language pathology health professional programs.Materials and Methods This was a quality improvement study using a cross sectional survey at a single institution. Separate student and tutor surveys were disseminated online. Students received the survey at two timepoints, tutors received the survey at one timepoint. Descriptive statistics summarized quantitative survey questions. Chi-squared or Fischer’s Exact analyses explored differences between in-person and virtual PBT. Exploratory analyses determined if associations amongst programs and/or between cohorts existed on variables of interest. Open-ended survey questions were analyzed using content analysis.Results A total of 241 students and 85 tutors completed the survey. Results demonstrate most students and tutors were satisfied with the use of virtual tutorials (77%; 89% respectively) and felt that they were effective in exploring content within the PBT course. However, students in the most recent cohort rated virtual tutorials more highly (p = 0.01).Conclusions Virtual modes of PBT were successful in achieving course objectives and led to high satisfaction in users. Health professional programs can use this information when designing virtual problem-based tutorial courses in the post-pandemic era.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".