Educating Nurse Practitioners on Factors Associated with Compassion Fatigue
Bibliographic record
Abstract
Nurse Practitioners (NPs) across Canada continue to endure the escalating demands of increasing patient workloads, the deficiency of inadequate resources, and the paucity of leadership support. The ongoing exposure to these and other challenges often contributes to a complex work environment that may result in compassion fatigue (CF). CF can be operationally defined as a state of exhaustion and dysfunction because of prolonged exposure to compassion stress and all that it evokes. The need for education and prevention of CF is critical as the prevalence of CF is on the rise. Guided by Watson’s Theory of Human Caring and Benner’s Theory of Novice to Expert, this project was conducted to determine if an educational intervention would increase the NPs knowledge and awareness on the signs and symptoms of CF and healthy coping strategies. NPs were recruited from a convenience sample of NPs belonging to a single acute care organization for this doctoral project and 35 NPs volunteered to participate in the educational intervention. Following a pretest survey, participants reviewed a PowerPoint presentation on educating NPs on the factors associated with CF and completed a posttest survey. A paired t-test indicated an increase in knowledge and awareness among NPs who participated in the educational intervention (t = -14.71, p < 0.001) indicating an increase in knowledge and (t = -50.61, p < 0.001) indicating an increase in awareness in CF. This project contributes to social change as it provides NPs with strategies to reduce and prevent CF clinically. Nurses who are equipped and understand strategies necessary to decrease work-related stress and CF tend to be more successful and can provide safe, competent patient care.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".