Feedback From Dental Students Using Two Alternate Coaching Methods: Qualitative Focus Group Study
Bibliographic record
Abstract
Background: Student feedback is crucial for evaluating the effectiveness of institutions. However, implementing feedback can be challenging due to practical difficulties. While student feedback on courses can improve teaching, there is a debate about its effectiveness if not well-written to provide helpful information to the receiver. Objective: This study aimed to evaluate the impact of coaching on proper feedback given by dental students in Saudi Arabia. Methods: A total of 47 first-year dental students from a public dental school in Riyadh, Saudi Arabia, completed 3 surveys throughout the academic year. The surveys assessed their feedback on a Dental Anatomy and Operative Dentistry course, including their feedback on the lectures, practical sessions, examinations, and overall experience. The surveys focused on assessing student feedback on the knowledge, understanding, and practical skills achieved during the course, as aligned with the defined course learning outcomes. The surveys were distributed without coaching, after handout coaching and after workshop coaching on how to provide feedback, designated as survey #1, survey #2, and survey #3, respectively. The same group of students received all 3 surveys consecutively (repeated measures design). The responses were then rated as neutral, positive, negative, or constructive by 2 raters. The feedback was analyzed using McNemar test to compare the effectiveness of the different coaching approaches. Results: While no significant changes were found between the first 2 surveys, a significant increase in constructive feedback was observed in survey #3 after workshop coaching compared with both other surveys (P<.001). The results also showed a higher proportion of desired changes in feedback, defined as any change from positive, negative, or neutral to constructive, after survey #3 (P<.001). Overall, 20.2% reported desired changes at survey #2% and 41.5% at survey #3 compared with survey #1. Conclusions: This study suggests that workshops on feedback coaching can effectively improve the quality of feedback provided by dental students. Incorporating feedback coaching into dental school curricula could help students communicate their concerns more effectively, ultimately enhancing the learning experience.
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".