Internet Cancer Information Use by Newly Diagnosed Individuals and Interactions With the Health System
Bibliographic record
Abstract
Nearly 40% of Canadians will be diagnosed with cancer in their lifetime, and people with cancer are increasingly turning to the Internet to bolster support and information received from health-care providers. However, little is known about the role of the Internet in patients’ interactions with the health-care system. The goals of this study are (1) to qualitatively explore the content of commonly used websites from a holistic nursing perspective, (2) to explore the prompts to use the Internet and how it informs the ways patients utilize and interact with health services, and (3) to document the types of Internet resources and amounts of usage. This study is guided by a constructivist mixed methods design. Interpretive description will guide the overarching qualitative component, including an analysis of data from commonly used websites and interviews with 16 newly diagnosed individuals. Open-ended interviews will clarify, through exploration, the role of the Internet in participants’ health system interactions. A survey of Internet use will add insight and depth about where, when, and how participants use the Internet. All interviews and website data will be analyzed using thematic analysis. Descriptive statistics will illustrate a summary of Internet usage. Triangulation of findings will provide oncology nurses and interdisciplinary team members with insight into how patients’ use of the Internet informs their use of health services. Methodologically, this study advances the use of qualitative methods for websites analysis, on which relatively little has been documented.
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How this classification was reachedexpand
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 | 0.014 | 0.002 |
| 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 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".