Compassionate nursing in challenging contexts: The importance of judgments
Bibliographic record
Abstract
BackgroundNurses' demonstration of compassion is an ethical and often regulatory expectation. While research has been conducted to examine the barriers and facilitators of compassion in nurses, little is known about how nurses develop and express compassion for patients who may be blamed for their health condition. Unvaccinated COVID-19 patients are an example of such patients.Research questionsHow do nurses provide compassionate care for unvaccinated adults infected with COVID-19? How did the context of COVID-19 vaccination in Canada shape nurses' relationships with unvaccinated patients?Research designA generic qualitative approach using interviews to gather data was used. Martha Nussbaum's conceptualization of compassion and its cognitive requirements was employed to add depth to the analysis.Participants and research contextSeventeen Registered Nurses, from a range of practice settings, who had cared for unvaccinated patients with COVID-19 participated.Ethical considerationsEthics approval was received, and signed informed consent was obtained. Participants who were the current students of the researchers were excluded.FindingsThree themes were identified:1) Encountering Extreme Workplace Impediments to Compassion.2) Managing Emotions to Provide "Nonjudgmental Care."3) Practicing Narrative Imagination.DiscussionThe difficult working conditions during the pandemic impeded nurses' capacity to be compassionate. Yet, none judged their patients' suffering as trivial, and all provided necessary nursing care. Some initially blamed these patients for the severity of their illness and suppressed their emotions to provide what they called "nonjudgmental care." Upon reflection, participants recognized that these patients' life circumstances may have been barriers to vaccination which, in the end, facilitated the development of compassion.ConclusionThis research has implications that go beyond that of caring for patients with COVID-19. The ideal of "nonjudgmental care" requires critical re-examination because judgments and emotions are required for compassion.
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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 | 0.036 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.054 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".