Nurse-led virtual prostate cancer clinic for survivorship care: Qualitative study of patient experiences and needs
Bibliographic record
Abstract
Abstract Background: Prostate cancer survivors (PCa) can experience a range of unmet needs over a long horizon of survivorship. Thin healthcare services and specialist-led follow-up care models may not adequately address these needs. Digitally mediated nurse-led virtual care models may help support PCa survivors who have unmet survivorship needs in current healthcare systems. Methods: This qualitative descriptive study explores 10 patients’ experiences with follow-up and virtual care, informing the design of a nurse-led virtual clinic (the “Ned Nurse” Clinic) to provide integrated healthcare services. Results: Patient follow-up care experiences included uncertainty regarding care gaps despite new telemedicine modalities and the need for personalized wellbeing support. Patient recommendations for virtual care related to improving integration of existing care connections, supporting patient self-management, and addressing accessibility. Patients anticipate nurse-led PCa virtual care will: (1) clarify patient and provider roles and responsibilities; (2) improve care ease and access to care; (3) enhance mental wellbeing, and (4) reinforce continuity of care. Conclusion: Patients are keen to benefit from the flexibility and increased resources that digital health may provide, but remain concerned about digital literacy and service changes to support these innovations. Future work should evaluate the efficacy of digitally mediated nurse-led virtual care models to support PCa survivorship.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".