Prevalence, causes, and consequences of moral distress in healthcare providers caring for people living with dementia in long-term care during a pandemic
Bibliographic record
Abstract
Healthcare providers caring for people living with dementia may experience moral distress when faced with ethically challenging situations, such as the inability to provide care that is consistent with their values. The COVID-19 pandemic produced conditions in long-term care homes (hereafter referred to as 'care homes') that could potentially contribute to moral distress. We conducted an online survey to examine changes in moral distress during the pandemic, its contributing factors and correlates, and its impact on the well-being of care home staff. Survey participants (n = 227) working in care homes across Ontario, Canada were recruited through provincial care home organizations. Using a Bayesian approach, we examined the association between moral distress and staff demographics and roles, and characteristics of the long-term care home. We performed a qualitative analysis of the survey's free-text responses. More than 80% of care home healthcare providers working with people with dementia reported an increase in moral distress since the start of the pandemic. There was no difference in the severity of distress by age, sex, role, or years of experience. The most common factors 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. At least two-thirds of respondents reported feelings of physical exhaustion, sadness/anxiety, frustration, powerlessness, and guilt due to the moral distress experienced during the pandemic. 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. Overall, we found that increases in moral distress during the pandemic negatively affected the well-being of healthcare providers in care homes, with preliminary evidence suggesting that individual and systemic factors may intensify the negative effect.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".