Managing Cancer Experiences
Bibliographic record
Abstract
BACKGROUND: People with cancer increasingly use the Internet to find information about their illness. However, little is known regarding people's use of cancer-related Internet information (CRII) to manage their patient experience, defined as patients' cumulative perceptions of interactions with the healthcare system during their illness. OBJECTIVE: The purpose of this study was to create an understanding of CRII use by people newly diagnosed with cancer and how it shapes their patient experience and informs their interactions with healthcare professionals and healthcare services. METHODS: An embedded mixed design guided this study. Nineteen people with cancer were interviewed twice and completed a survey about CRII use. Qualitative data were analyzed using thematic analysis. Descriptive statistics summarized the quantitative findings. RESULTS: Participants of all ages and educational levels reported using CRII as a pivotal resource, across the cancer trajectory. Cancer-related Internet information played a central role in how patients understood their illness and when they sought and used healthcare services. Two themes emerged based on patient interviews: (1) person in context and (2) management of information. CONCLUSION: Cancer-related Internet information plays a crucial role in how people manage their illness and take control of their patient experience. Participants used CRII to learn about their illness, support their efforts to self-manage, and complement information from professionals. IMPLICATIONS FOR PRACTICE: Individuals and institutions can promote and encourage tailored CRII use by engaging patients and suggesting websites based on their needs. Doing so may create efficiencies in service use and empower patients to be more involved in their own care.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".