The Absence of Funeral Rites as a Risk Factor for the French Bereaved Population
Bibliographic record
Abstract
During the COVID-19 pandemic, stringent measures were imposed in numerous countries, including France. These measures significantly disrupted societal practices, particularly mourning and funeral rituals. This study, conducted between June and September 2021 as part of the COVIDEUIL-France research, involved 242 participants, predominantly female (84%) with an average age of 49.64 years. The research aimed to investigate the consequences of the absence of funeral rituals and the adoption of personalized, domestic, and digital alternatives. Using online surveys and psychological tools, including the Traumatic Grief Inventory Self Report Version (TGI-SR), General Health Questionnaire-28 (GHQ-28), and Post-traumatic Growth Inventory (PTGI), the study found a significant correlation between the lack of traditional practices and grief complications. Despite 21.81% of participants compensating with personal commemorations, digital commemorations were underutilized, raising questions about their effectiveness. The study provides crucial insights, emphasizing the need to address the psychological effects of disrupted mourning practices in France.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".