The psychological impact of a cancer diagnosis on families: The influence of family functioning and patients' illness characteristics on depression and anxiety
Bibliographic record
Abstract
A diagnosis of cancer is a very stressful event for the patients and their families. Patients, partners and other family members can suffer from clinical levels of depression and severe levels of anxiety and stress reactions. The similarity in levels of distress between patients and partners and patients and offspring suggests that there are common factors that impact on families' distress levels. The current study examined levels of depression and anxiety in newly diagnosed adult patients (n = 48) and their adult relatives (n = 99). Family functioning and patients' illness characteristics were identified as factors that might impact on families' depression and anxiety. Results from multilevel models indicated that family functioning was important. Families that were able to act openly, express feelings directly, and solve problems effectively had lower levels of depression. Direct communication of information within the family was associated with lower levels of anxiety. Aside from differences anxiety due to cancer type, patients' illness characteristics appear to be risk factors in patients' but not relatives' depression and anxiety. The results from the current study suggest that researchers and clinicians need to be family-focused as cancer affects the whole family, not just the patient.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".