SEARCHING FOR MEANING IN LOSS: ARE CLINICAL ASSUMPTIONS CORRECT?
Bibliographic record
Abstract
Three assumptions guiding research and clinical intervention strategies for people coping with sudden, traumatic loss are that (a) people confronting such losses inevitably search for meaning, (b) over time most are able to find meaning and put the issue aside, and (c) finding meaning is critical for adjustment or healing. We review existing empirical research that addresses these assumptions and present evidence from a study of 124 parents coping with the death of their infant and a study of 93 adults coping with the loss of their spouse or child to a motor vehicle accident. Results of these studies indicate that (a) a significant subset of individuals do not search for meaning and yet appear relatively well-adjusted to their loss; (b) less than half of the respondents in each of these samples report finding any meaning in their loss, even more than a year after the event; and (c) those who find meaning, although better adjusted than those who search but are unable to find meaning, do not put the issue of meaning aside and move on. Rather, they continue to pursue the issue of meaning as fervently as those who search but do not find meaning. Implications for both research and clinical intervention are discussed.
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 | 0.043 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.070 |
| Scholarly communication | 0.010 | 0.032 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.008 | 0.017 |
| 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".