A review of the evaluation of clinical teaching: new perspectives and challenges <sup>*</sup>
Bibliographic record
Abstract
PURPOSE: This article discusses the importance of the process of evaluation of clinical teaching for the individual teacher and for the programme. Measurement principles, including validity, reliability, efficiency and feasibility, and methods to evaluate clinical teaching are reviewed. CONTEXT: Evaluation is usually carried out from the perspective of the learner. This article broadens the evaluation to include the perspectives of the teacher, the patient and the institutional administrators and payers in the health care system and recommends evaluation strategies. RESULTS: Each perspective provides specific feedback on factors or attributes of the clinical teacher's performance in the domains of medical expert, professional, scholar, communicator, collaborator, patient advocate and manager. Teachers should be evaluated in all domains relevant to their teaching objectives; these include knowledge, clinical competence, teaching effectiveness and professional attributes. CONCLUSIONS AND IMPLICATIONS: Using this model of evaluation, a connection can be made between teaching and learning about all the expected roles of a physician. This can form the basis for systematic investigation into the relationship between the quality of teaching and the desired outcomes, the improvement of student learning and the achievement of better health care practice. It is suggested that the extent of effort and resources devoted to evaluation should be commensurate with the value assigned to the evaluation process and its outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".