Organizational Interventions to Support Second Victims in Acute Care Settings: A Scoping Study
Bibliographic record
Abstract
OBJECTIVES: Health care providers that experience harm after adverse events have been termed "second victims." Our objective was to characterize the range and context of interventions to support second victims in acute care settings. METHODS: We performed a scoping study using Arksey and O'Malley's process. A library scientist searched PubMed, Cumulative Index of Nursing and Allied Health, EMBASE, and Cochrane Central Register of Controlled Trials in September 2017, and updated the search in November 2018. We sought gray literature (Canadian Electronic Library, Proquest and Scopus) and searched reference lists of included studies. Stakeholder organizations and authors of included studies were contacted. Two reviewers independently reviewed titles and abstracts and extracted data. A qualitative approach was used to categorize the context and characteristics of the 22 identified interventions. RESULTS: After screening 5634 titles and abstracts, 173 articles underwent full-text screening. Twenty-two interventions met the criteria and were categorized as providing peer support (n = 8), proactive education (n = 6), or both (n = 8). Programs came from Canada (n = 2), Spain (n = 2), and the United States (n = 18). A specific traumatic event triggered the development of 5 programs. Some programs used a standard definition of second victims, (n = 6), whereas other programs had a broader scope (n = 12). Confidentiality was explicitly assured in 9 peer support programs. Outcome measures were often not reported. CONCLUSIONS: This is a new area of study with little qualitative data from which to determine whether these programs are effective. Many programs had a similar design, based on the structure proposed by the same small group of experts in this new field. Concerns about potential legal proceedings hinder documentation and study of program effectiveness.
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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 | 0.039 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".