Ways of knowing on the Internet: A qualitative review of cancer websites from a critical nursing perspective
Bibliographic record
Abstract
People diagnosed with cancer typically want information from their doctor or nurse. However, many individuals now turn to the Internet to tackle unmet information needs and to complement healthcare professional information. The purpose of this study was to qualitatively explore the content of commonly searched cancer websites from a critical nursing perspective, as this information is accessible, and allows patients to address their information needs in ways that healthcare professionals cannot. This qualitative examination of websites is informed by Carper's fundamental patterns of knowing and complemented with the critical view to technology espoused by the philosophy of technology. We conducted a review of 20 websites using a two-step interpretive descriptive approach and thematic analysis. We identified the dominant discourse to be focused on empirical information on treatment, prognosis, and cure, and a paucity of sociopolitical, ethical, personal, and esthetic information. In place of holistic, nuanced, and accurate knowledge nurses may provide, patients find predominantly empirical and biomedical information online. Discussion explores and critiques online cancer content, gaps in information, and the importance of information diversity. Implications focus on needed discourse around pervasive technologies and the nursing role in assessing and directing patients to holistic information.
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".