Expressing grief through metaphors: family caregivers’ experience of care and grief during the Covid-19 pandemic
Bibliographic record
Abstract
PURPOSE: The COVID-19 pandemic has disrupted thousands of individuals' experience of caregiving and grief. This qualitative study aimed to gain in-dept understanding of family caregivers' lived experiences of caregiving and bereavement in the context of the COVID-19 pandemic in Quebec, Canada. The study also aimed at providing new insight about caregiving and bereavement by analysing the metaphors family caregivers use to report their experiences. METHODS: The design of this study was guided by an interpretative phenomenological approach. In-depth interviews were conducted with twenty bereaved family caregivers who had lost a loved one during the first waves of the pandemic. RESULTS: Results indicate that bereaved family caregivers lived and understood their experience in terms of metaphoric cut-offs, obstructions and shockwaves. These three metaphors represented the grief process and the bereaved's quest for social connection, narrative coherence and recognition. CONCLUSION: By identifying the meaning of the bereaved's metaphors and the quest they reveal, our study underlines the singularity of pandemic grief and points to the value and meaning of caregiving with regard to the grieving process.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".