Adolescent narrative comments in assessing medical students
Bibliographic record
Abstract
BACKGROUND: Adolescent medical interviewing is a difficult topic to teach and assess. Programmatic assessment has been gaining interest in medical teaching, and shifts the mode of assessment from the traditional assessment of learning (e.g. written exams) to the assessment for learning (e.g. feedback). The Structured Communication Adolescent Guide (SCAG) is a programmatic assessment tool that allows an adolescent patient to provide three types of feedback (written, numeric, grade) to a medical student in an authentic clinical workplace. METHODS: We conducted a qualitative analysis of written narrative feedback from SCAGs completed by non-standardised adolescent patients interviewed by third-year medical students. SCAG numerical scores and grades were compared between the positive and the negative written narrative feedback. RESULTS: Thirty-seven (50%) of 74 SCAGs had written narrative feedback. 'Approachable' and 'confidentiality concerns' were the most common positive and negative written comments, respectively. The 'teen-only communication' SCAG section, containing the HEADSS (Home, Education, Activities, Drugs, Suicide, Sex) portion of the interview, had the highest number of negative comments. All of the positive comments had A grades (100%), whereas the negative comments had A (58%), B (37%) and C (5%) grades. The 'teen-only communication' and 'initiating the interview' SCAG sections had significantly lower numerical scores assigned to negative feedback (p = 0.023, p < 0.001). Adolescent medical interviewing is a difficult topic to teach and assess DISCUSSION: Confidentiality concerns remain a top priority for undergraduate medical education training in adolescent patient interviewing. Written narrative feedback is extremely valuable as teens can provide both positive and negative comments. This is in contrast to adolescent patients most often over-inflating grades or scores to all learners, which can mislead the student.
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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 | 0.007 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".