Comparing Survivors of Cancer in Population-Based Samples With Those in Online Cancer Communities: Cross-sectional Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: Most Western countries have websites that provide information on cancer and the opportunity to participate in online cancer communities (OCCs). The number of patients with cancer that participate in these OCCs is growing. These patients are relatively easy to approach for research purposes. OBJECTIVE: The objective of this study is to determine the differences and similarities between survivors of cancer in population-based samples and survivors participating in OCCs who use the internet in relation to their illness. METHODS: In 2017, we drew a sample of 539 population-based patients and 531 OCC patients. The population-based patients were sent a paper-based questionnaire, and the OCC patients were sent the same questionnaire on the web. In the questionnaire, we asked patients about their sociodemographics, internet use, sources of information, media use, and wishes regarding future internet use for health care-related purposes, and the effect of internet use on their health care consumption. RESULTS: The response rate of population-based internet users was 47% (233/496), and that of the OCC group was 40.3% (214/531). The OCC group had a significantly higher education level (P<.001), was younger (P<.001), had more survivors that were employed (P<.001), and attached greater importance to the internet (171/214, 79.9% vs 126/233, 54.1%; P<.001) and fellow survivors (107/214, 50% vs 60/233, 25.8%; P<.001). Compared with the population-based group, the OCC group reported more intensive internet use immediately after diagnosis, during treatment, and during follow-up (P<.001 in each case). There were similarities in terms of the relative importance that survivors attach to the various sources of information, the topics on which they seek information, and their wishes for future eHealth possibilities. The OCC group reported a greater need to participate in a web-based class or chat with others (92/214, 43% vs 44/233, 18.9%). CONCLUSIONS: We conclude that survivors who are members of an OCC are not representative of survivors of cancer in general. There are significant differences in sociodemographic characteristics, internet use during their treatment journey, internet search frequency during their cancer journey, and participation wishes. Using web-based information and communication can support shared decision-making and may facilitate the active participation of patients during their treatment. For research purposes, it is important to take the bias in OCC groups into account.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".