Information Needs and Sources of Information for Patients during Cancer Follow-Up
Bibliographic record
Abstract
BACKGROUND: Now more than ever, cancer patients want health information. Little has been published to characterize the information needs and preferred sources of that information for patients who have completed cancer treatment. METHODS: We used a nationally validated instrument to prospectively survey patients attending a cancer clinic for a post-treatment follow-up visit. All patients who came to the designated clinics between December 2011 and June 2012 were approached (N = 648), and information was collected only from those who agreed to proceed. RESULTS: The 411 patients who completed the instrument included individuals with a wide range of primary malignancies. Their doctor or health professional was overwhelmingly the most trusted source of cancer information, followed by the Internet, family, and friends. The least trusted sources of information included radio, newspaper, and television. Patients most preferred to receive personalized written information from their health care provider. CONCLUSIONS: Cancer survivors are keenly interested in receiving information about cancer, despite having undergone or finished active therapy. The data indicate that, for patients, their health care provider is the most trusted source of cancer information. Cancer providers should ask patients about the information they want and should direct them to trusted sources.
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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 | 0.003 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".