Performance of speech-language pathology students in problem-based learning tutorials and in clinical practice
Bibliographic record
Abstract
The purpose of the study was to identify if performance of speech-language pathology students in problem-based learning (PBL) tutorials could predict subsequent clinical performance evaluated through (a) a non-standardized, custom clinical evaluation form (HKU form) and (b) a standardized competency assessment for speech pathology developed in Australia (COMPASS®). Students' scores from PBL tutorial performance were correlated with scores in clinical placement on both the HKU form and the COMPASS. Significant correlations were found between students' PBL tutorial performance (reflective journals and participation in the tutorial process) and their clinical performance (treatment and interpersonal skills) on the HKU clinical evaluation form. Significant correlations were also found between (a) PBL tutorial performance (participation in the tutorial process) and their clinical performance (all generic and occupational competencies, and the overall score) on the COMPASS, (b) PBL tutorial performance (reading forms) and two occupational competencies on the COMPASS, (c) PBL tutorial performance (reflective journals) and four occupational competencies and the overall score on the COMPASS. The results highlighted the need for validating the assessment for the learning process in PBL tutorials with empirical evidence and the advantage of assessing clinical performance through COMPASS in Hong Kong. Tutors, clinical supervisors and students should be given clear behavioral descriptors for expected performance in PBL tutorials and clinical practice at different year levels.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".