Properties of moral distress experienced by Canadian intensive care unit nurses during the COVID-19 pandemic: An interpretive descriptive study
Bibliographic record
Abstract
Background & Purpose: In response to the multitude of ethical issues that arise in the delivery of care provided in intensive care units (ICUs), nurses working in this setting frequently experience moral distress. The properties of moral distress have been well defined. However, within the context of the coronavirus-disease 2019 (COVID-19) pandemic, less is known about the properties of moral distress experienced by ICU nurses. This subsequently affects the advancement of our knowledge, specifically of effective mitigative interventions for moral distress. The purpose of this analysis is to describe the key properties of moral distress experienced by ICU nurses during the COVID-19 pandemic. Methods & Procedures: Guided by interpretive descriptive design, a purposeful sample of 40 Canadian ICU nurses described their experiences of moral distress within the context of their practice during the COVID-19 pandemic. Data generated included the administration of a demographic questionnaire and the Measure of Moral Distress – Healthcare Providers survey, and 1:1 semi-structured virtual (telephone or videoconference) interviews (May – September 2021). Analysis was informed by the tenents of reflexive thematic analysis and rapid qualitative analysis. Results: Nurses experienced moral distress under the complex interplay of two overarching, broad conditions: (1) when nurses’ voices, driven by efforts to optimize patient care at an exceptionally high standard, were not heard; and (2) when patients received substandard levels of care, that was not patient-centered, pain free, or that did not align with organizational, professional, or personal standards. These two broad conditions were influenced by three sub-conditions: (1) lack of respect for nurses’ expert knowledge; (2) cultures and systems of communication and (3) responses to safety and staffing. Discussion: Moral distress experienced by Canadian ICU nurses is a complex phenomenon. These findings advance and refine our knowledge of key components of moral distress. We identified the conditions that generate moral distress for nurses, and the properties of the antecedent moral events. Future approaches to mitigate moral distress need to address the broad conditions under which moral distress occurs. Key words: COVID-19, nursing, intensive care, moral distress, nursing ethics
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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 | 0.023 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".