Caught in the net: Characterizing how testicular cancer patients use the internet as an information source
Bibliographic record
Abstract
INTRODUCTION: Over 70% of Canadians who use the internet search for healthcare information online. This is especially true regarding the young adult population. Testicular cancer is the most commonly diagnosed cancer in men aged 15-29. This study characterizes how testicular cancer patients access healthcare information online, and how this influences their clinical encounters and treatment decisions. METHODS: From June 2018 to January 2019, a survey consisting of 24 open- and close-ended questions was distributed to testicular cancer patients at a tertiary cancer center. Survey results were evaluated using mixed methods analysis. RESULTS: Fifty-nine surveys were distributed and 44 responses were received. All respondents used the internet regularly and 82% used the internet as a source of information regarding their cancer. The majority followed top hits from Google when selecting websites to view. Frequent topics searched included treatment details and survivorship concerns. Eighty-nine percent of users found online information easy to understand and 94% found it increased their understanding. For 47% of users, the internet did not influence their clinical consultation nor their treatment decision (53%). CONCLUSIONS: Most testicular cancer patients in this study are regular internet users and use the internet to search for testicular cancer information. Healthcare providers should recognize this, and can play important roles in discussing online findings with patients to assess their background knowledge and expectations, as well as providing guidance on selecting credible online resources. The results of this study can be used to improve patient-physician communication and education.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".