Does Assessment Method Matter in Detecting Mental Health Distress among Ashkenazi and Mizrahi Israeli Women with Breast Cancer?
Bibliographic record
Abstract
Authors examined differences in assessment method (structured diagnostic interview versus self-report questionnaire) between ethnic groups in the prevalence of mood and anxiety disorders among women with breast cancer. A convenience sample of 88 Mizrahi (Jews of Middle Eastern/North African descent, n = 42) and Ashkenazi (Jews of European/American descent, n = 46) women with breast cancer from oncology units in three health centers across Israel participated in the study. Participants were within eight months of diagnosis. Participants completed the Hospital Anxiety and Depression Scale (HADS) and a structured diagnostic interview, the Mini-International Neuropsychiatric Interview (MINI). Approximately one-third (31.8 percent, n = 28) of participants were diagnosed with at least one mood or anxiety disorder based on the MINI. Significantly more Mizrahi participants (42.9 percent) were diagnosed with at least one mood or anxiety disorder, compared with their Ashkenazi counterparts (21.7 percent). Mean score on HADS was below the optimal cutoff score (≥13) among all participants, with no significant difference in mean score for emotional distress based on HADS between the two ethnic groups. The findings highlight the role of measurement variance in assessing mental health distress among women with breast cancer in general and among ethnic and racial minorities in particular.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".