Evaluating peer support as an intervention to reduce the adverse sequelae associated with ethical dilemmas
Bibliographic record
Abstract
Background: Regular exposure to ethical dilemmas can impact a nurse’s well-being and by extension, the healthcare system. Peer support programs are interventions that have been implemented to mitigate these effects. CARED rounds are a local peer support program that has not been evaluated since implementation in 2020 and thus, the efficacy in addressing ethical dilemmas is unknown. Purpose: I aimed to evaluate CARED rounds’ efficacy in addressing the adverse consequences of ethical dilemmas and determine the potential benefits, challenges and opportunities for improvement. Methods: I completed a literature review, consultation with stakeholders, and an evaluation of CARED rounds, including a questionnaire and interviews. The Moral Distress Scale-Revised (MDS-R) captured moral distress levels. Results: Peer support programs, including CARED rounds, have promoted resilience and camaraderie among participants by validating their feelings, decreasing feelings of isolation, and improving job satisfaction. Moderate moral distress levels were observed in both participant and non-participant groups, suggesting that while CARED rounds offer some support, broader sources of distress could remain. Key barriers to attendance included scheduling conflicts, staff workloads, and insufficient understanding of CARED rounds’ benefits among non-participants. Conclusion: Most registered nurses, regardless of participation status report moderate moral distress levels suggesting that CARED rounds offer some support but may not fully address broader sources of moral distress. Addressing logistical challenges and enhancing communication about the program’s benefits could improve participation and ensure greater alignment with its intended design.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".