Information Needs and Sources of Information for Patients during Cancer Follow-Up
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it