Use of online resources by patients with cancer: The Canadian experience.
Bibliographic record
Abstract
318 Background: Canadians are among the highest users of the Internet. Online health information resources are easily accessible and provide quick information, but concerns exist about their role within a physician–patient relationship. Methods: Cancer patients across multiple disease sites, recruited from a regional cancer center, were interviewed regarding their internet use for health-related data. Descriptive statistics characterized online resource usage. Univariate and multivariate logistic regression models evaluated the association between socio-demographics, functional status, clinico-pathological variables and internet usage. Results: Of 191 patients in an interim analysis, 87% had home internet access. Google was the most commonly accessed website (79%), followed by cancer society websites (43%), Mayo Clinic (32%) and Wikipedia (28%). Disease-specific information (91%) was more commonly researched than information about specific physicians (30%). As expected, being married, having completed high school, earning a higher income and having home internet access were each associated with accessing information online (p<0.05). Patients were more likely to access disease-specific information through eMedicine than Wikipedia. Younger patients were more likely to evaluate support group information (aOR=5.9, 95%CI [1.6-21.3], p=0.02) and cancer society websites (aOR=2.6 [1.2-5.4], p=0.04). More educated patients used cancer society (aOR=2.7 [1.3-6.0], p=0.03) and subscription websites (aOR=3.8 [1.6-8.8], p=0.01). Surgical patients used subscription websites more than non-surgical patients (aOR=3.9 [1.7-8.8], p=0.006), obtaining disease-specific information (aOR=5.0 [1.4-18.5], p=0.04). Conclusions: Cancer patients commonly acquire health information from search engine queries. Socio-demographic and clinico-pathological variables affect online information access among cancer patients. Oncologists need to consider the potential benefits and pitfalls of patient online use in cancer management and in their physician-patient communication and shared decision making. Recruitment ends in August 2012 and data on the full 400 patient cohort will be presented. GL and SCG are co-senior authors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".