Online education needs and preferences of patients with cancer and their caregivers.
Bibliographic record
Abstract
e21669 Background: Patients and caregivers have many questions upon receiving a diagnosis of cancer. One way to help alleviate this burden, and enable participation in care is through education. Patient education correlates with higher levels of satisfaction and improved clinical outcomes. However, little is known about how patients and caregivers define their education needs. Methods: We surveyed patients with cancer and caregivers to assess their perceived education needs and resources. Results:2327 individuals representing 9 malignancies participated in the survey. Participants were predominantly patients (72%), with a majority (51%) having received their initial diagnosis of cancer within the past 2 years. Less than a quarter (22%) of respondents felt their educational needs were being completely met by available resources. Respondents were most likely to seek out information at the time of diagnosis (24%) and to increase understanding of treatment options (24%). Conversely, individuals were least likely to seek education upon disease progression (7%). In all malignancies examined, the 2 topics considered most important were treatment options (70%-89%) and understanding test results (74%-87%) while information about how to take medication as prescribed was deemed least important (46%-67%). Conclusions: Our study identified a lack of educational materials available and designed to meet cancer patients’ needs on the internet. Given that few patients and caregivers reported that their educational needs are being met, efforts may be needed to ensure that those patients who want to receive education to become informed patients can find what they need, when they need it.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".