Second Victims and Patient Safety: A Scoping Review
Bibliographic record
Abstract
Justification: Health professionals perform their duties in environments that require complex care, and avoiding possible complications is the main focus, however, given human fallibility, adverse events sometimes become inevitable, and when this occurs, the effects go far beyond the individual who suffered the damage directly, as there is an indirect effect on the health professionals involved, who are considered "second victims". Considering that the likelihood of these professionals suffering physical and psychosocial damage because of the harm caused to the patient is relatively high, it is essential that studies are developed to provide adequate support for these professionals. Objective: To review the national and international literature to identify existing notes on the support given to health professionals in the role of second victim. Results: 27 articles dated between 2011 and 2021 were analyzed. The country with the most publications was the United States of America, followed by Canada and Spain, and quantitative methods were prevalent. The studies identified important strategies, such as sensitivity, empathy and adequate support, effective communication, review of adverse events, social and emotional support in a trusting environment, individualized follow-up and a support network. By adopting these strategies, it is possible to provide effective and compassionate support, helping them to face and overcome the emotional and psychological challenges associated with adverse events or medical errors. Conclusions: In view of the impacts faced by healthcare professionals in the role of second victim, a sensitive approach to the issue is needed, and it is essential to recognize and address the emotional needs of professionals with support strategies, psychological support and resilience education programs. And so, promote a culture of safety in healthcare institutions, encouraging constant learning and welcoming a just culture.
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 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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".