Impact of the Media and the Internet on Oncology: Survey of Cancer Patients and Oncologists in Canada
Bibliographic record
Abstract
PURPOSE: To evaluate the use of the news media and the Internet as sources of medical information by patients and oncologists in Canada and to investigate the impact on patients' treatment decisions and the patient-doctor relationship. PATIENTS AND METHODS: During a 2-week period, 191 ambulatory patients participated in the survey. Questionnaires were also mailed to Canadian oncologists: 410 of 686 questionnaires were returned (response rate = 60%). RESULTS: Of the 191 patients, 86% wanted as much information as possible about their illness, 54% reported receiving insufficient information, 83% cited physicians as their primary information source, and 7% cited the Internet. Seventy-one percent of patients actively searched for information, and 50% used the Internet. Patients' opinions about the balance, accuracy, and relevance of news media reports were evenly split. English as the first language, access to the Internet, and use of alternative treatments predicted a higher rate of information seeking. Most oncologists routinely pay some attention to medical news and believe that it is difficult for patients to interpret medical information in the media and on the Internet accurately. Both patients and oncologists agree that information seeking does not affect the patient-physician relationship. CONCLUSION: Information searching is common among cancer patients in Canada. It does not affect the patient-doctor relationship. The media and the Internet are powerful means of medical information dissemination. Strategic efforts are needed to improve the quality of medical news reporting by the media, and to provide guidance for patients to understand their disease and interpret such information better.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".