Results of a feasibility study for a psycho‐educational intervention in head and neck cancer
Bibliographic record
Abstract
BACKGROUND: With survival rates for people with head and neck (H&N) cancers static during the past 30 years and the enormous burden of psychosocial impacts they suffer well documented, the testing of psychosocial interventions in this group is a priority. OBJECTIVE: To test the feasibility of providing a psycho-educational intervention for people with H&N cancer. METHODOLOGY: A prospective non-randomised design was used. Subjects were patients with H&N cancer. They were offered the Nucare coping strategies program in one of three formats: small group and one-to-one formats with therapists; and a home format, with material for home use, without a therapist. Outcomes measures (quality of life (QOL) and anxiety and depression) were collected at baseline and following the intervention. Analyses were performed using non-parametric statistics. RESULTS: Of 128 people invited to participate, 66 agreed, 59 completed the intervention and 50 had outcomes data. Following the intervention, there were significant improvements in physical and social functioning and global QOL, and reduced fatigue, sleep disturbance and depressive symptoms. CONCLUSIONS: These data suggest that the intervention is desired by the target group, feasible to deliver after cancer therapy and may have some beneficial effects, although an appropriately designed study is required to confirm this.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".