Rituals in Grief: Why Meaning Matters More than Numbers
Bibliographic record
Abstract
The COVID-19 pandemic severely restricted social interactions and ritual practices, deeply affecting mourning rituals. This longitudinal study investigates how prevented and performed rituals influence grief reactions evolution among bereaved individuals in the province of Quebec (Canada). Data were gathered via online surveys conducted five times (every six months) from March 2021 to May 2023. With 529 participants at baseline, we assessed their experiences with pre- and postmortem rituals, satisfaction levels, and grief reactions using the Traumatic Grief Inventory Self Report (TGI-SR). At the start, 32% of participants with a loss occurring ≥ 6 months showed probable Prolonged Grief Disorder (PGD), which decreased to 13.9% by the second data collection. Participants had an average of 2.9 out of eight traditional rituals prevented by the pandemic. Those with probable PGD reported significantly lower satisfaction with performed rituals compared to those with fewer grief reactions. Mixed models analysis indicated that the number of prevented rituals did not predict TGI-SR scores, but satisfaction with performed rituals significantly predicted grief trajectories. Higher satisfaction was associated with lower grief reactions over time. These results suggest that meaningful ritual experiences can help reduce grief intensity even when the number of rituals is limited. The study demonstrates the adaptability of bereaved individuals during the pandemic and emphasizes that the quality, rather than the quantity, of rituals is crucial in the grieving process. This underscores the need to support the facilitation of meaningful rituals to aid those grieving.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".