Impact of the COVID‐19 pandemic on moral distress of care home staff caring for people with dementia
Bibliographic record
Abstract
Abstract Background Healthcare providers caring for people living with dementia may experience moral distress when faced with an ethically challenging situation, such as an inability to provide care that is consistent with their values. The COVID‐19 pandemic both created new and exacerbated pre‐existing factors which contributed to the experience of moral distress amongst healthcare providers in LTCHs. Method We conducted an online survey to examine changes in mortal distress during the first wave of the pandemic, its contributing factors and correlates, and its impact on the well‐being of LTCH staff. A total of 227 health care providers from LTCHs across Ontario, Canada completed the online survey. Using a Bayesian approach, we examined the associations between moral distress and staff demographics and roles, and characteristics of the LTCH in which they worked. Result More than 80% of LTCH healthcare providers working with people with dementia reported an increase in moral distress since the start of the pandemic. They frequently experienced physical and emotional symptoms related to moral distress more than once a week, with the most common symptoms being physical exhaustion (55%) powerlessness (39%), sadness/anxiety (38%) frustration/anger (31%), and difficulty sleeping (33%). There was no difference in the severity of distress by age, sex, role, or years of experience. The most common situations associated with moral distress were lack of activities and family visits, insufficient staffing and high turnover, and having to follow policies and procedures that were perceived to harm residents with dementia. Respondents working in not‐for‐profit or municipal homes reported less sadness/anxiety and feelings of not wanting to go to work than those in for‐profit homes. Front‐line staff were more likely to report not wanting to work than those in management or administrative positions. In the survey’s free‐text responses, respondents elaborated on the emotional impact of the change in care delivery for LTCH residents. Conclusion Moral distress increased during the pandemic, negatively affecting the well‐being of healthcare providers in LTCHs, with preliminary evidence suggesting that individual and systemic factors may intensify this negative effect.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".