Multimedia information intervention and its benefits in partners of the head and neck cancer patients
Bibliographic record
Abstract
We aimed to investigate the levels of anxiety, depression, satisfaction with information provision and cancer-related knowledge in partners of head and neck cancer (HNC) patients receiving a Multimode Comprehensive Tailored Information Package (MCTIP). A non-randomised, controlled trial was conducted with partners of HNC patients recruited at two academic hospitals in Montreal. The Test participants received the MCTIP, while the Control participants received information in an ad hoc manner. All participants were evaluated using the Hospital Anxiety and Depression Scale (HADS), Satisfaction with Cancer Information Profile and a cancer knowledge questionnaire at baseline, and 3 and 6 months later. Data were analysed using descriptive statistics, t-test and chi-square test, and mixed model analysis to test the impact of the intervention. A total of 31 partners of HNC patients participated in this study and completed all the evaluations. The partners in the Test group experienced significantly lower levels of anxiety (P = 0.001) and depression (P = 0.003) symptoms and were more satisfied (P = 0.002) with cancer information provided than partners in the Control group. Providing tailored information seems to have positive outcomes regarding anxiety, depression, and satisfaction in partners of HNC patients. Larger randomised studies are warranted to validate these effects.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".