The Meaning of Loss Codebook: Construction of a System for Analyzing Meanings Made in Bereavement
Bibliographic record
Abstract
Recent research on grieving populations has emphasized the role of meaning making in adaptation to bereavement, typically relying on simple self-reports of the extent to which respondents have been able to find sense or benefit in their loss. The present article reports the development of a reliable and comprehensive coding system for analyzing meanings made in the wake of the death of a loved one, yielding a 30-category codebook demonstrating excellent reliability, and comprising both negative and positive themes that arise as grievers attempt to make sense of loss. Based on an intensive qualitative analysis of a diverse sample of 162 adults mourning the natural or violent death of a variety of loved ones, the Meaning of Loss Codebook could prove useful in process-outcome studies of grief therapy, analysis of naturalistic first-person writing about bereavement experiences in grief diaries and blogs, and clinical assessment of meanings made in the course of bereavement support or professional intervention.
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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 | 0.029 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".