Small programs, big challenges: Reimagining the evaluation of clinical teaching in genetic counseling
Bibliographic record
Abstract
Within the health professions education system, a significant proportion of teaching and learning occurs in the clinical setting. As such, the need to measure effective teaching for accreditation standards, faculty development, merit pay, academic promotion, and for monitoring the safety of the learning environment has led to numerous universities developing instruments to evaluate teaching effectiveness in this context. To date; however, these instruments typically focus on the student perspective, despite evidence demonstrating that student evaluations of teaching (SETs) lack correlation with learning outcomes and are not a true measure of teaching effectiveness. This issue is further exacerbated in small health professional training programs, such as genetic counseling, where clinical teachers may only supervise 1-3 students per year. As a result, not only are SETs more confounded due to small sample sizes, but a direct conflict exists between respecting learner anonymity and providing timely and relevant feedback to faculty. In such contexts, even using SETs to evaluate the nature of the learning environment may be unreliable due to student concerns about identifiability and fear of retaliation for unfavorable evaluation. This paper will review the literature regarding SETs, barriers to this process within the clinical setting, and the unintended downstream consequences. Options for addressing issues related to the use of SETs will be considered, with particular focus on the process of reflection and the use of teaching consultations or peer support groups as a means to improve teaching effectiveness in this learning environment.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".