Feasibility, Acceptability and Clinical Utility of the Bereavement and Grief Cultural Formulation Interview for Prolonged Grief Disorder
Bibliographic record
Abstract
Prolonged grief disorder (PGD) is a new diagnostic category included in global diagnostic classification systems for mental disorders. However, PGD can only be diagnosed if the severity and duration exceed socio-cultural norms. Here, we present a new supplementary module to the DSM-5 Cultural Formulation Interview: the Bereavement and Grief Cultural Formulation Interview (BG-CFI). The BG-CFI was developed to help clinicians provide a culturally informed diagnosis and guide treatment planning.We investigated the feasibility, acceptability, and clinical utility of the BG-CFI. Two participant groups (11 refugees, asylum seekers or migrants experiencing bereavement and 3 clinicians) took part in the study and were interviewed using open-ended questions on measures of feasibility, acceptability, and clinical utility. A step-by-step procedure was followed: (1) Clinicians and/or researchers conducted the BG-CFI with participants; (2) Debriefing interviews were conducted separately with clinicians and with bereaved participants.The BG-CFI was found to be a feasible, acceptable, and clinically useful tool for both bereaved participants and clinicians. Where clinicians found the interview difficult to conduct (i.e. lack of conceptual clarity or triggering emotional distress) specific changes were made to the interview format such as prompts for further questioning or recommendations for withholding or adapting questions. The BG-CFI would offer a useful complement for a reliable assessment of PGD in clinical settings working with cultural incongruity.
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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 | 0.050 | 0.075 |
| 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 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".