Bibliographic record
Abstract
METHODS: In the stepped wedge design, participating sites were randomly allocated from Control to Training then Intervention conditions. Thirty-seven health professionals completed manual-based training and skill development before delivering up to four therapy sessions to 70 patients with HADS scores of 8 to 21. The primary outcome was difference in HADS scores from baseline to 10-week follow-up. Secondary outcomes were quality of life (FACT-G; EQ-5D), supportive care needs (Supportive Care Needs Survey), and Demoralisation (Demoralisation Scale). RESULTS: Baseline measures were obtained for 469 patients. The majority were female (70%) and married, and 32.8% had advanced disease. Mean HADS scores were 8.8 (SD = 6.30) and 8.6 (SD5.90) for Intervention and Control groups, respectively (p = 0.59). At follow-up, there was no significance difference in total HADS scores between Control and Intervention groups. Higher baseline depression score was predictive of improvement (p < 0.001). Improvement in anxiety was predicted by higher baseline anxiety score (p < 0.001) and lower FACT functional well-being score (p < 0.001). Patients with advanced disease were more likely than those with early disease to experience reduction in supportive care needs. CONCLUSIONS: Frontline health professionals can provide psychosocial care, but interventions should target those most likely to benefit rather than being generically applied. Research Implications: These results provide preliminary evidence of the characteristics of patients who are most likely to benefit from a brief psychosocial intervention integrated into clinical care. Further analysis is required of the specific types of therapy which are most likely to be of benefit for depressed cancer patients. Practice Implications: Integration of psychosocial care into routine cancer care can be achieved through a model of care in which frontline health professionals who have participated in focused training and skill development provide brief tailored therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 0.001 |
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 teacher head, 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".