“I couldn’t”: A phenomenological exploration of ethical tensions experienced by bereaved family members during the pandemic
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic entailed significant changes in accompaniment, end-of-life, and bereavement experiences. In some countries, public health measures prevented or restricted family caregivers from visiting their dying loved ones in residences, long-term care institutions, and hospitals. As a result, family members were faced with critical decisions that could easily lead to ethical dilemmas and moral distress. AIM: This study aimed to understand better the experience of ethical dilemmas among family caregivers who lost a loved one. METHDS: We interviewed twenty bereaved family caregivers and analysed their narratives using Interpretative phenomenological analysis. RESULTS: Our analysis suggests that family caregivers struggled with their multiple responsibilities (collective, relational, and personal) and had to deal with the emotional cost of their choices. Results display three emerging themes describing the experience of ethical struggles: (1) Flight or fight: Struggling with collective responsibility; (2) Being torn apart: Assuming relational responsibility and (3) "Choosing" oneself: The cost of personal responsibility. DISCUSSION/CONCLUSION: Results are discussed and interpreted using an ethical, humanistic, and existential conceptual framework.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.024 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| 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".