Distress and Psychosocial Needs of a Heterogeneous High Risk Familial Cancer Population
Bibliographic record
Abstract
In order to assess the levels of distress and psychosocial support needs of a high risk population, we undertook a study to look at both the objective and subjective levels of distress and the wants and needs of individuals from a high familial cancer risk population. Three hundred and eighteen individuals (160 affected, 158 unaffected) completed several distress and psychosocial needs questionnaires (including the Brief Symptom Inventory-18). Sixty key informants were also surveyed about their perspective on the support needs of this population. In the largely female (90%), largely HBOC syndrome group (approximately 90%), 20% had significant levels of generalized distress, with no significant differences between affected and unaffected individuals. Generalized distress was also not significantly different as a function of mutation status. Individuals who received inconclusive test results, however, were more likely to indicate somatic symptoms of distress. Those individuals who did not have social support were more likely to be those who had never had cancer and who either had a mutation, received inconclusive test results, or were not tested. Key informants were most likely to indicate that patients need more support. These results provide evidence for the importance of establishing regular psychosocial distress screening, including a focus on somatic symptoms, in such high risk populations.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".