Cross-cultural generalizability of the ICD-11 PGD symptom network: Identification of central symptoms and culturally specific items across German-speaking and Chinese bereaved
Bibliographic record
Abstract
BACKGROUND: Prolonged grief disorder (PGD) is a newcomer to psychopathology and the new ICD-11 diagnostic criteria are conceptualized with an eye towards global applicability. Yet, previous network studies have not used official ICD-11 criteria nor tested whether network structures generalize across cultural groups even though much current research relies on ICD-11 PGD criteria. METHODS: To overcome these limitations, the present study used data from 539 German-speaking (n = 214) and Chinese (n = 325) bereaved individuals to investigate similarities and differences in network structures of ICD-11 PGD criteria. In addition, network structures were investigated for an expanded supplementary questionnaire of culturally-bound grief symptoms hypothesized to be of relevance in each cultural context. RESULTS: Results suggested both similarities and differences in network structures between the two samples. Across cultural groups, intense feelings of sorrow and inability to experience joy or satisfaction since the death emerged as most central symptoms. Compared to the standard PGD network, the expanded network showed a better average predictability for Chinese participants, but no improvement for the German-speaking context. Unhealthy behavior change was the most central symptom for Chinese bereaved when additional grief symptoms were included. CONCLUSIONS: Results of the present study suggest there are culturally-bound symptoms of grief which are not included in the current ICD-11 PGD criteria. These findings provide areas of special clinical attention concerning screening and treatment and present a first step towards a more cultural-sensitive understanding of grief. CLINICAL TRIALS: NCT03568955.
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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 | 0.002 | 0.006 |
| 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 | 0.000 |
| 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".