Impact of French lockdowns on bereavement experiences: Insight from <scp>ALCESTE</scp> analysis revealing psychological resilience and distinct grief dynamics amidst <scp>COVID</scp>‐19
Bibliographic record
Abstract
At the beginning of 2020, the entire world was shocked by a global health emergency. According to the literature, fear, high mortality and health restrictions had significant psychological consequences on the population. This study evaluates the French lockdown's impact on the grieving process and how people worked through their grief. Two semi-structured interviews were conducted with 31 participants who had lost a loved one between March 2020, June, and September 2021 (T0) and 6 months later (T1). Subsequently, they were divided into two groups: those who lost someone during the first lockdown (Group 1) and those who lost someone outside the lockdown periods (Group 2). The interviews were analysed using the ALCESTE software, a statistical analysis tool for textual data based on word co-occurrences. This research significantly advances the understanding of bereavement during crises, providing new perspectives and practical insights for policymakers, healthcare professionals and support organisations. Its methodological innovation and detailed analysis contribute to the ongoing discussion on grief and resilience in challenging circumstances. Ultimately, this study lays the foundation for improved support and intervention strategies tailored to the needs of bereaved individuals during crises.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".