Physiotherapy students’ rating on lecturers’ and supervisors’ clinical education attributes
Bibliographic record
Abstract
Background: Clinical education is considered a vital aspect of education of health science students. Attributes of clinical educators play a crucial role in determining the outcome of clinical teaching and learning. A good clinical educator ensures that students get maximum benefits of the clinical learning experience. Objective: To determine the ratings of physiotherapy students on clinical education attributes of lecturers and clinical supervisors. Methods: The study was conducted with 81 clinical physiotherapy students from two universities in Ghana. Two copies of McGill clinical teachers’ evaluation (CTE) tool were used to obtain students’ ratings on their clinical supervisors’ and lecturers’ clinical education attributes. Independent t-test was used to compare the means of students’ level of study and ratings regarding the clinical education attributes of clinical supervisors and lecturers. Results: Students had a high rating on their clinical education attributes of supervisors and lecturers with a mean score of ([Formula: see text]) and ([Formula: see text]), respectively. Rating on clinical education attributes of supervisors ([Formula: see text]) and lecturers ([Formula: see text]) did not differ significantly between the different levels of study. Conclusion: Clinical physiotherapy students rated the clinical education attributes of their lecturers and supervisors high.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".