Beautifully broken: Implementing a peer support program to help healthcare providers heal
Bibliographic record
Abstract
Introduction: This evidence-based project aimed to determine the feasibility of implementing a peer support program to minimize trauma in healthcare professionals (HCP)s following unanticipated adverse events. Based on the forYOU Program designed by Sue Scott at the University of Missouri Health System, this program trained peers to offer real-time caring and support to other clinicians coping with such events. Most healthcare professionals are involved in at least one adverse event in their careers. Albert Wu, MD (2000) coined the term second victim to capture the essence of the trauma experienced by healthcare professionals when an unanticipated event negatively impacts a patient. When left unchecked, this trauma can result in moral distress, stress disorders, and burnout as the clinician ruminates over the event. Providing emotional support has improved second victims' emotional well-being and recovery. Therefore, healthcare leaders are encouraged to develop comprehensive programs to provide easy access to peer and social support when they experience an adverse event.Methods: Designed for implementation in the Women's Service Department of a 350-bed southwestern hospital, this project employed a pre-/post-evaluation of subjective outcomes using an online survey for nurses. A core group of trainers attended a two-day peer support train-the-trainer event hosted by the forYOU Program at the University of Missouri Health Care System. This group trained 26 peer supporters representing the four departments in Women's Services and both shifts. Baseline data was collected (n = 44) to assess the frequency and impact of unanticipated adverse events, the perceived support, and the type of support received. Following the four-month implementation in the Summer/Fall of 2020, post-data was obtained, including a program awareness assessment (n = 17).Results: Pre- and post-implementation of the Peer Support Program, nurses in Women's Services reported adverse events impacting their emotional well-being. Post-program, more nurses reported receiving support (86% post-program versus 43% pre-program). Before employment, 79% of nurses who received support received peer support, versus 86% receiving peer support post-implementation. The implementation occurred during the COVID pandemic, which may have resulted in a decreased post-assessment sample size. However, the peer supporters reported hesitancy in completing encounter forms feeling that providing support was “too personal”. The participants said that they found the peer support program worthwhile.Conclusions: Nurses on the implementation units indicated receiving more support after the peer support program was implemented and felt the program was beneficial. Since unanticipated events are inevitable in health care, the steering committee recommended sustaining and spreading the program to all the nursing departments. More data is needed to determine the full impact of the program.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".