Together, alone: Personal experiences of virtual funeral attendance during the COVID-19 global pandemic
Bibliographic record
Abstract
The COVID-19 pandemic caused profound distortions in how deaths were marked by those left to mourn their passing. Public health restrictions prohibited gatherings of friends and families for traditional funerals, causing an upsurge in reliance on virtual alternatives. The aim of this study was to explore the experiences of individuals attending virtual funerals following a death of any cause, including reasons for choosing virtual attendance, perceived differences relative to in person attendance, and the extent to which mourning practices were accommodated. Between May 2021 and June 2022, we identified 57 participants with virtual funeral attendance experience. They identified many shortcomings of virtual funerals, captured under themes including, socialization, community support, sharing food, physical contact, consoling the bereaved, sharing memories, and connection. There were features of virtual funerals that participants appreciated, summarized by themes including, accessibility, taking part or marking the event, closure, technological advantages and privacy. Despite a sense that virtual funerals provided an opportunity to grieve “together, alone,” most conceded it was better than not being able to take part at all. This study provides detailed information about participating in virtual funerals, identifying features of this experience that should be examined to determine how those may influence grieving processes and bereavement outcomes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".