Differentiating Symptoms of Complicated Grief and Depression among Psychiatric Outpatients
Bibliographic record
Abstract
OBJECTIVE: This study examined whether dimensions of complicated grief (CG) could be distinguished from dimensions of depression and whether these dimensions were differentially affected by group psychotherapy for CG. METHOD: A total of 398 psychiatric outpatients who had experienced one or more significant death losses provided ratings on standard measures of grief and depression. Factor analysis of the 56 items from these measures was used to explore the possibility that grief and depression symptoms would form separate dimensions of distress. Subsamples of the patients also participated in 1 of 2 forms of short-term group therapy for CG. Repeated-measures analysis of variance and calculation of effect sizes were performed to examine changes in the dimensions following treatment. RESULTS: The grief items formed 3 distinct clusters representing different dimensions of CG. None of the depression items loaded highly on these grief dimensions. The depression items formed 2 distinct clusters. Two of the grief dimensions demonstrated the most improvement following group therapy that addressed CG. There was also evidence for differential effectiveness of the 2 forms of group therapy. CONCLUSIONS: When assessing psychiatric patients who have death losses, clinicians should consider different types of grief reactions. Different types of grief reactions may be responsive to different treatments. In the absence of depressive symptoms, clinicians should not assume the absence of CG.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".