A qualitative study on healthcare professional and patient perspectives on nurse-led virtual prostate cancer survivorship care
Bibliographic record
Abstract
BACKGROUND: Virtual nurse-led care models designed with health care professionals (HCPs) and patients may support addressing unmet prostate cancer (PCa) survivor needs. Within this context, we aimed to better understand the optimal design of a service model for a proposed nurse-led PCa follow-up care platform (Ned Nurse). METHODS: A qualitative descriptive study exploring follow-up and virtual care experiences to inform a nurse-led virtual clinic (Ned Nurse) with an a priori convenience sample of 10 HCPs and 10 patients. We provide a health ecosystem readiness checklist mapping facilitators onto CFIR and Proctor's implementation outcomes. RESULTS: We show that barriers within the current standard of care include: fragmented follow-up, patient uncertainty, and long, persisting wait times despite telemedicine modalities. Participants indicate that a nurse-led clinic should be scoped to coordinate care and support patient self-management, with digital literacy considerations. CONCLUSION: A nurse-led follow-up care model for PCa is seen by HCPs as acceptable, feasible, and appropriate for care delivery. Patients value its potential to provide role clarity, reinforce continuity of care, enhance mental health support, and increase access to timely and targeted care. These findings inform design, development, and implementation strategies for digital health interventions within complex settings, revealing opportunities to optimally situate these interventions to improve 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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".