Intersections Between Grief and Trauma: Toward an Empirically Based Model for Treating Traumatic Grief
Bibliographic record
Abstract
Two divergent areas of study have focused on the experiences of grief, i.e., bereavement, and on trauma and its aftermath. The grief literature has its foundations in psychodynamic and relational theories, and thus treatment modalities have focused on resolving relationship issues through reminiscence and developing a new sense of the relationship and of the self, independent of the lost loved one. The trauma literature, while having some psychodynamic roots, has been founded primarily on biological and cognitive formulations. Again, while many different treatments are discussed, cognitivebehavioral approaches based on cognitive restructuring and symptom management dominate the practice efficacy literature. But trauma and bereavement/loss are not mutually exclusive, and when a practitioner is faced with a client suffering from both, it is necessary to attempt to integrate these divergent theories and at times antithetical treatment approaches. This paper therefore seeks to address the issue of treatment efficacy in traumatic loss and develop guidelines for evidence-based approaches to practice. [Brief Treatment and Crisis Intervention 4:289–309 (2004)]
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".