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 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.683 | 0.371 |
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".