Resilience to Loss and Chronic Grief: A Prospective Study from Pre-loss to 18 months Post-Loss
Bibliographic record
Abstract
The vast majority of bereavement research is conducted after a loss has occurred.Thus, knowledge of the divergent trajectories of grieving or their antecedent predictors is lacking.This study gathered prospective data on 205 individuals several years prior to the death of their spouse and at 6-and 18-months postloss.Five core bereavement patterns were identified: common grief, chronic grief, chronic depression, improvement during bereavement, and resilience.Common grief was relatively infrequent, and the resilient pattern most frequent.The authors tested key hypotheses in the literature pertaining to chronic grief and resilience by identifying the preloss predictors of each pattern.Chronic grief was associated with preloss dependency and resilience with preloss acceptance of death and belief in a just world.The death of a spouse is generally assumed to be one of the most stressful experiences that people encounter during the course of their lives (Holmes & Rahe, 1967).However, there are marked individual differences in how much and for how long people grieve (Bonanno & Kaltman, 1999, 2001;Wortman & Silver, 1989, 2001).In addition to what is assumed to be the typical or common reaction, an initial increase in depression that gradually subsides over time, several other patterns of grief have been discussed in the literature.These include prolonged or chronic grieving, the noticeable absence of grief symptoms, and delayed grief responses.Social and personality psychologists have become increasingly interested in these different trajectories, and how they compare with those observed for other marital transitions and other stressful life events (e.g.,
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".