Using Provider Education About Self Care to Reduce Compassion Fatigue Among Nurses
Bibliographic record
Abstract
Many have scrolled through Facebook and news stories highlighting nurses as “heroes” of the current coronavirus pandemic. Pictures of nurses in personal protective equipment while at work or clips of nurses making fun tiktok video’s celebrating recovered COVID -19 patients. Social media does not capture the sheer emotional, physical, and spiritual wear that nurses experience providing compassionate care to patients facing light threatening illness or events. Health care organizations are under pressure to control cost, increase productivity, and increase patient satisfaction scores, all while facing a pandemic crisis. This type of pressure can create inadequate staffing and increase clinical responsibilities for the nurse. An atmosphere that creates the foundation for compassion fatigue and nurse burnout. Compassion fatigue is linked to poor personal health, nursing retention and recruitment rates, and quality of patient care with increased safety and medication errors (Registered Nurses’ Association of Ontario, 2011). Raising awareness of Compassion Fatigue in nursing is vital to improving patient outcomes and addressing the “dying” roll of the bedside nurse.
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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 | 0.003 | 0.011 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".