Let people have their people: Exploring death and grieving during COVID-19
Bibliographic record
Abstract
,Background: The Covid-19 pandemic had a profound impact on the landscape of bereavement. The effects have been acute and far-reaching, especially as they pertain to impacts on the grief process. Method: A qualitative approach, known as Interpretive Description, was used to explore the experiences of individuals who lost a loved one to death during the highest restrictive period of the Covid 19 pandemic. This restrictive period was inclusive of March 18, 2020 to June 30, 2021 (Faye et al., 2022). Eight participants across northern British Columbia (BC) participated in semi-structured interviews and shared their experiences around the death of a loved one during the noted restrictive period. Braun and Clarke’s (2006) six phases of thematic analysis were used to analyse the data which generated the findings. Results: There were five overarching themes that emerged from the data with 15 subthemes. The five overarching themes followed by the subthemes included— family dynamics (amplified family dynamics and difficult decisions), individual impacts (death rituals, isolation, and complex grief), societal impacts (exacerbated social conditions, accountability, distrust of systems and fear), coping (spirituality, technology, creative expressions, gratitude and forgiveness), and hope for the future (seeking solutions and information sharing). Conclusion: The resounding experiences of those who encountered the death of a loved one during the highest restrictive period of the Covid-19 pandemic were fraught with difficulties, in addition to the death. A call for improved solutions in the future was a strong narrative throughout the experiences provided.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.019 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".