Experiences of Moral Distress in Canadian Intensive Care Unit Professionals During and After the COVID-19 Pandemic: A Qualitative Exploratory Multiple Case Study in Ontario and Alberta, Canada
Bibliographic record
Abstract
Background: Since the beginning of the COVID-19 pandemic, moral distress among healthcare workers in the Intensive Care Unit (ICU) has garnered both media and academic attention. Moral distress has been theorized as occurring when individuals are constrained from doing what they perceive as morally right. This study sought to empirically examine the lived experiences of moral distress among clinical and administrative healthcare professionals in a sample of Canadian ICUs during the COVID-19 pandemic. Methods: Qualitative case study methodology was used as the overarching approach, collecting and comparing data from two distinct cases: one ICU in Ontario and one in Alberta. Data collection involved two primary sources: semi-structured interviews with staff and document review of institutional and government directives to provide contextual data. Data analysis commenced concurrently with data collection, and generated within- and across-case themes, as well as allowed descriptive accounts of moral distress. Results: Thirty-six healthcare workers across two sites were interviewed. Participants described three primary categories of constraints leading to moral distress. These were: 1) The rapidity and opaqueness of policy development, specifically pertaining to 2) the implementation of family visitation and treatment triage decisions, and 3) resource shortages, which reduced patient interactions, shifted professional responsibilities. Each of these constraints yielded circumstances and forced decisions that were perceived as morally wrong because they compromised care quality and outcomes. Conclusions: While sharing similarities with the growing literature on moral distress in the context of the COVID-19 pandemic, this study reveals new insights on how provincial and institutional policy has direct bearing on experiences of moral distress. Policies and circumstances forced ICU staff to choose between actions they considered the most right and the least wrong. Understanding these specific policy-driven constraints highlights the need for healthcare systems and processes that mitigate moral distress and sustain our health workforce.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.045 | 0.017 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".