Education on whistleblowing in medicine: A scoping review
Bibliographic record
Abstract
Background: Whistleblowing is critical to promoting patient safety, ethical accountability, and systemic improvement. Despite its importance, medical trainees often face cultural, organizational, and personal barriers to speaking up. While medical education plays a key role in shaping future physicians’ preparedness to whistleblow, the scope and effectiveness of existing educational approaches remain unclear. Objective: This scoping review aimed to explore how whistleblowing is currently taught, supported, or assessed in undergraduate and postgraduate medical education, and to identify gaps and opportunities for curricular development. Methods: Following the JBI methodology and PRISMA-ScR guidelines, we conducted a comprehensive search across six databases (MEDLINE, Embase, ERIC, Scopus, Web of Science, PsycINFO) and grey literature sources. Studies were eligible if they addressed educational strategies related to whistleblowing among medical students, residents, or early-career physicians. After removing 1,208 duplicates from an initial yield of 3,742 records, 2,534 titles and abstracts were screened. A total of 78 articles underwent full-text review, and 13 met all inclusion criteria. Results: Thirteen peer-reviewed studies were included. Thematic analysis revealed four major domains: (1) educational strategies, (2) learner attitudes and preparedness, (3) institutional and cultural contexts, and (4) virtue ethics and empowerment. More than half of the articles described education that addressed whistleblowing implicitly within professionalism or patient safety modules. Students commonly reported fear, uncertainty, and lack of institutional support as barriers. Programs with an intended emphasis on ethics, emphasized moral development, institutional transparency, and psychological safety showed more promising outcomes. Conclusion: Whistleblowing education in medical education remains limited, inconsistently implemented, and rarely evaluated. Few studies explicitly assessed learners’ knowledge, skills, or preparedness to whistleblow, with most relying on indirect measures such as attitudes or moral reasoning. To better prepare future physicians, curricula should integrate explicit whistleblowing content, incorporate robust assessment strategies, foster ethical resilience, and be supported by institutional cultures that empower and protect those who speak up.
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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".