Learning About Grief Triggers Through an Exploratory-Descriptive Study
Bibliographic record
Abstract
A qualitative study was undertaken to identify what triggers grief in the first two years following the death of a beloved family member, determine how often triggered grief occurs, and gain lived insight into what can be done (if anything) to manage triggers and also triggered grief. Four themes highlighting an uncertain process associated with grief triggers were identified: (a) my whole life was grief, (b) frequently hit by “hard-grief” triggers, (c) reaching a balance with grief and grief triggers to absorb the losses and reshape life, and (d) shifting to good and welcome memories, triggers that keep the person alive. These themes are described, with quotes illustrating their relevance for advising bereaved people about the grief triggers they may encounter. This evidence adds to a limited body of evidence on grief triggers and offers new insights for developments in grief theory and bereavement programs or services.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".