The Grief and Meaning Reconstruction Inventory (GMRI): Initial Validation of a New Measure
Bibliographic record
Abstract
Although increasing numbers of grief theorists, researchers, and therapists have begun to focus on the quest for meaning in lives disrupted by loss, no convenient and psychometrically validated measure of meanings made specifically in bereavement has been available to guide their efforts. To construct such a measure, the authors began with a systematic content analysis of sense-making, benefit finding, and identity reconstruction themes gleaned from the narrative responses of a sample of 162 adults who were diverse in their age, ethnicity, relationship to the decedent, cause of death, and severity of their grief response. These were then formulated into a set of 65 candidate items in a Likert scale format representing the level of the respondent's endorsement of the item in the past week. Subsequent administration to a second sample of 300 bereaved respondents permitted factor analysis of this pilot version of the Grief and Meaning Reconstruction Inventory (GMRI), and reduced the items to 29, which loaded on 5 distinct factors, labeled Continuing Bonds, Personal Growth, Sense of Peace, Emptiness and Meaninglessness, and Valuing Life. Both the overall GMRI and its constituent factors showed good internal consistency and strong convergent validity in the form of negative correlations with established measures of bereavement-related negative emotions, symptoms of complicated grief, and more general psychological distress and mental health symptomatology, and positive correlations with grief related personal growth. The authors close by noting several specific research and clinical applications of the measure, which could play a useful role in testing and refining contemporary models of meaning made in the wake of 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 | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".