POWER AND DISCLOSURE IN HEALTHCARE: A SCOPING REVİEW OF MEDICAL ERROR RESPONSE ACROSS SYSTEMS
Bibliographic record
Abstract
Background: Medical errors remain as a troubling cause of preventable harm within healthcare systems. In response, error disclosure has emerged as both an ethical imperative and a quality improvement priority. Despite increasing institutional and legal support for transparency, disclosure practices are often hindered by emotional, organizational, and systemic barriers. Objective: This scoping review consolidates evidence on medical error disclosure and response, with particular attention to strategies that address power dynamics, communication practices, legal protections, organizational culture, and community engagement. Methods: A curated body of peer-reviewed literature from 2000 to 2025 was analyzed using an integrative and interpretive approach. Sources were drawn from medicine, public health, ethics, law, and education, and categorized across ten thematic domains: disclosure and communication; support for healthcare professionals; systematic learning; safety culture; legal and ethical considerations; restorative approaches; surveillance; education; interdisciplinary insights; and community-led models. Results: Findings highlight that effective disclosure is supported by structured frameworks (e.g., CANDOR), legal protections (e.g., apology laws), and organizational policies that promote psychological safety. Emerging innovations include patient-partnered education, virtual training modules, interdisciplinary root cause analysis, and community-informed participatory models. However, gaps remain in sustainability, long-term outcomes, and integration of equity and power-sensitive approaches. Conclusions: Medical error response is evolving from isolated clinician responsibility to an integrated, systems-based practice. Sustainable progress requires alignment of institutional culture, legal reform, emotional support, and patient engagement. By embracing relational, transparent, and justice-oriented frameworks, healthcare systems can transform error disclosure into a meaningful catalyst for healing, accountability, and trust
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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 | 0.036 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.025 | 0.027 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".