Virtual follow‐up care among breast and prostate cancer patients during and beyond the <scp>COVID</scp>‐19 pandemic: Association with distress
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to investigate associations between self-reported distress (anxiety/depression) and satisfaction with and desire for virtual follow-up (VFU) care among cancer patients during and beyond the COVID-19 pandemic. METHODS: Breast and prostate cancer patients receiving VFU at an urban cancer centre in Toronto, Canada completed an online survey on their sociodemographic, clinical, and technology, characteristics and experience with and views on VFU. EQ5D-5 L was used to assess distress. Statistical models adjusted for age, gender, education, income and Internet confidence. RESULTS: Of 352 participants, average age was 65 years, 48% were women,79% were within 5 years of treatment completion, 84% had college/university education and 74% were confident Internet users. Nearly, all (98%) had a virtual visit via phone and 22% had a virtual visit via video. The majority of patients (86%) were satisfied with VFU and 70% agreed that they would like VFU options after the COVID-19 pandemic. Participants who reported distress and who were not confident using the Internet for health purposes were significantly less likely to be satisfied with VFU (OR = 0.4; 95% CI: 0.2-0.8 and OR = 0.19; 95% CI: 0.09-0.38, respectively) and were less likely to desire VFU option after the COVID-19 pandemic (OR = 0.49; 95% CI: 0.30-0.82 and OR = 0.41; 95% CI: 0.23-0.70, respectively). CONCLUSIONS: The majority of respondents were satisfied with VFU and would like VFU options after the COVID-19 pandemic. Future research should determine how to optimize VFU options for cancer patients who are distressed and who are less confident using virtual care technology.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".