How reminiscing about deceased close others together with continuing bonds relates to grief severity and personal growth: a cross-sectional study with bereaved adults
Bibliographic record
Abstract
OBJECTIVES: One of the reasons why people engage in reminiscences about their past is to maintain intimacy with deceased close others. Although previous research alerts to the negative effects of reminiscence for intimacy maintenance on mental health, little is known about its relation to individuals' reactions to loss (i.e. grief severity and personal growth). In two samples, we focus on time since loss and continuing bonds, to elucidate the role of reminiscence for intimacy maintenance in grief. METHOD: The samples comprised 111 and 198 bereaved adults. All participants rated the frequency of reminiscence for intimacy maintenance and loss-related variables, such as time since loss, continuing bonds, and grief severity. Sample 2 additionally completed measures of personal growth, loss-centrality, and their interconnectedness with the deceased. RESULTS: Reminiscence on intimacy maintenance was positively related to grief severity. This relation was independent of time since loss and partly driven by externalized bonds. Internalized bonds mediated the relation between reminiscence for intimacy maintenance and grief severity (in sample 1) and personal growth (in sample 2). CONCLUSION: Continuing bonds help explain why reminiscing for intimacy maintenance can be harmful in terms of grief severity but also fosters personal growth after the loss.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".