A systems approach to address the impact of second victim phenomenon
Bibliographic record
Abstract
Over the last decade, second victim phenomenon (SVP) has been identified as a serious issue for healthcare workers (HCWs). Results from a 2018 survey of Canadian HCWs demonstrated that the majority of those who responded had experienced SVP and indicated that there was a lack of support in the workplace. The overall objectives of this paper are to a) heighten the awareness about SVP and its impact on HCWs and 2) to recommend an organizational/systems approach to support HCWs as second victims. This will be accomplished by first defining SVP and its relationship to patient safety. We will apply a health geography framework which incorporates the concepts of location, place, human interaction, movement and region to demonstrate the variability across care settings and the need for a systems approach to support HCWs. A human geography approaches to SVP would allow policymakers, leadership teams and managers within a health care setting to uniquely tailor their support systems to their individual contexts, which in turn will create a workplace culture of safety that builds on the organization's unique qualities.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".