Fostering Resilience over Multiple-losses for Nursing Staff in the Palliative Care Unit: Whole Person Approach – Part 1
Bibliographic record
Abstract
Objectives: “Bereavement overload” due to multiple losses is one of the stressors for the nursing staff working at Palliative Care Unit (PCU), which may be especially tough to those with less exposure to such situation. A support program was developed for the nursing staff of newly-opened PCU (April 2011) in order to foster resilience and wellness despite multiple losses. We conducted a study to evaluate the effectiveness of the support program with “whole person approach” – consisting of 3 modules; 1) lecture on grief and bereavement (mind), 2) experiential workshop on body awareness and relaxation (body/spirit), and 3) group discussion (mind/spirit), for the increased sense of self-efficacy, awareness of their inner healing power, and fostering mutual understanding and support.Methods: 20 nurses were randomly assigned to two groups for the action research project. Data included participant observation, individual and focus group interviews with one of the investigators. The support program package was offered from October to December 2012 (A) and from January to March 2013 (B) respectively, using wait-list control method. Self-efficacy scale was used at the base line, at the completion of package A, and at the completion of package B. Participants also answered brief survey after each module, followed by semi-structured interview.Results: The self-efficacy score initially showed decline of both groups (intervention/control) , reflecting the “tough two months” with the highest number of the total deaths as well as deaths within 5 days post admission. However, intervention group showed more gradual decline comparing to the control group, plus higher elevation 3 months later, which may indicate some effect of the program.Conclusions: The support program was positively received and contributed to the nursing staff’s increased sense of self-efficacy and resilience over “bereavement overload.” Continued program development is in progress based on the feedback.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".