Medical students as agents of change: a qualitative exploratory study
Bibliographic record
Abstract
BACKGROUND: There is evidence that medical students have the potential to actively initiate, lead and bring about change through quality improvement within healthcare organisations. For effective change to occur, it is important that students are introduced to, and exposed to the value and necessity of quality improvement early in their careers. The aim of this study was to explore the perspectives and experiences of medical students and their mentors after undertaking quality improvement projects within the healthcare setting, and if such practice-based experiences were an effective way of building improvement capacity and changing practice. METHODS: A qualitative interpretive description methodology, using focus groups with medical students and semi-structured interviews with academic and clinical mentors following completion of students' 4-week quality improvement projects was adopted. RESULTS: The findings indicate that there are a range of facilitators and barriers to undertaking and completing quality improvement projects in the clinical setting, such as time-scales, differing perspectives, roles and responsibilities between students and multidisciplinary healthcare professionals. CONCLUSIONS: This study has demonstrated that quality improvement experiential learning can develop knowledge and skills among medical students and transform attitudes towards quality improvement. Furthermore, it can also have a positive impact on clinical staff and healthcare organisations. Despite inherent challenges, undertaking quality improvement projects in clinical practice enhances knowledge, understanding and skills, and allows medical students to see themselves as important influencers of change as future doctors.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".