The importance of bereavement cognitions on grief symptoms: Applications of cognitive processing therapy
Bibliographic record
Abstract
The experience of bereavement and trauma share some overlapping features, such as changes in the cognitive processing of information. This article explored the extent to which cognitive processes during bereavement influence coping outcomes in relation to grief in a sample of university students who had been previously bereaved (N = 104). First, we examined differences in bereavement outcomes based on whether cognitive processes associated with the bereavement were accommodated (n = 55), over-accommodated (n = 25), or assimilated (n = 24). Results indicate that grief-related outcomes significantly differed as a result of cognitive processes. We then compared the degree to which these cognitive processes accounted for grief outcomes in individuals with high and low grief symptoms. In individuals with low grief levels, both depressive symptoms and grief cognitions significantly accounted for grief levels. However, in individuals with high grief levels, only cognitive processes significantly accounted for levels of grief. Results from this study underscore the importance of examining cognitive processes during bereavement. Future research should further examine the underlying mechanisms that contextualize both the bereavement and cognitive processes surrounding the loss. Finally, results from this study highlight the associated cognitive processing of information as a potential topic for targeted treatment in bereavement.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".