Bibliographic record
Abstract
Objective: Student focus groups have often been used for undergraduate curriculum planning and evaluation. They have not, however, been used in the design or delivery of faculty development. The goal of this initiative was to use the results of student focus groups in planning a faculty development workshop for small-group tutors. Description: Small-group teaching sessions were introduced into our undergraduate medical curriculum in a systematic way in 1994. In 1998, we began using student focus groups, designed to assess students' perceptions of effective small-group teaching, to plan a faculty development workshop and to give feedback to small-group tutors. To date, ten focus groups have been held with 86 students, representing the four years of the undergraduate curriculum. During each focus-group session, student representatives were asked to identify the characteristics of effective small groups, to highlight the positive attributes of small-group tutors, to describe the ingredients of effective cases, and to discuss strategies for student evaluation in the small-group setting. Student feedback highlighted the value of certain tutor characteristics, a nonthreatening group atmosphere, clinical relevance, and the use of pedagogic materials that encourage independent thinking and problem solving. Based on the results of the focus-group discussions, we developed the content of a faculty development workshop designed to improve small-group teaching and created appropriate teaching materials for small-group tutors (e.g., overheads and handouts). Discussion: Faculty members' reactions to the students' feedback and suggestions were very positive. The workshop participants were intrigued by the students' insight, and many asked for copies of the workshop overheads, based on student feedback, to be distributed to colleagues who did not attend. In addition, a number of the workshop participants requested assistance in conducting focus groups with their own students. As faculty developers, we were impressed by the attention workshop participants gave to the students' comments and suggestions, which were remarkably consistent with the literature.1 The value of student feedback in this format was also demonstrated during our planning of a refresher course on effective lecturing, when several members of the planning committee suggested that we once again canvass student opinion by using focus groups. The results of this preliminary experience suggest that focus groups with students can be valuable in designing faculty development activities, that faculty members are responsive to student concerns, and that this method can be adapted to diverse settings. We would now like to conduct similar focus groups with residents and CME participants, and use this method to evaluate the success of some of our faculty development activities designed to improve small-group teaching.
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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 | 0.066 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".