Virtual Funerals During COVID-19 and Beyond
Bibliographic record
Abstract
Abstract Physical distancing measures and the restrictions on large group gatherings following the COVID-19 pandemic have left many to consider alternative approaches to commemorating the death of a loved one. Advancements in information technologies and the availability of affordable electronic devices have brought forth the ability to use virtual platforms to host funeral services for those unable to be with their loved ones. The aim of this study was to identify existing and potential online platforms for hosting a virtual funeral, explore the safety considerations of hosting a service during the pandemic and share the experiences of individuals who have previously hosted a virtual service. To conduct the research, an environmental scan was undertaken searching academic, grey literature and online websites. The results showed that there are currently several online platforms made specifically for virtual services and many free public platforms that can be used. Death services must ensure staff who are in direct contact with the deceased have proper Personal Protective Equipment and companies must adhere to regulations regarding group gatherings, screening for symptoms and physical distancing. Some individuals expressed having a positive experience, stating that the virtual service felt more intimate, while others expressed difficulty in navigating the technology, particularly the older adult attendees. Virtual funeral services may prove to be a practical and safer alternative during COVID-19 and may provide some comfort for those facing such challenging times. By examining existing platforms and their use, an opportunity exists to generate recommendations for additional supports, particularly for older adults.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".