Survey of computer use for health topics by patients with head and neck cancer
Bibliographic record
Abstract
BACKGROUND: Computers are potentially powerful tools for patient education. E-health, which refers to health services and information delivered through the Internet, is a growing phenomenon within the health-care field. We sought to describe computer use and interest in e-health resources among patients with head and neck cancer. METHODS: A questionnaire was administered to 207 patients with head and neck cancer attending oncology follow-up clinics at a single comprehensive cancer center. RESULTS: Forty-eight percent had never used a computer; 43% used one more than once a week. E-health information had been sought by 31%. Likelihood to access e-health information increased with education and income but decreased with age (p < or = .05). CONCLUSIONS: Many patients with head and neck cancer welcome information technology, but most prefer more traditional sources of information. Interventions to improve computer access and/or skills are largely undesired. Individuals seem to either embrace technology or not. In this respect, patients with head and neck cancer are similar to, rather than unique from, other patients with cancer.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".