Nurse-led virtual prostate cancer clinic for survivorship care: Qualitative study of healthcare professional experiences and needs
Bibliographic record
Abstract
Abstract Background: Approximately one in eight Canadian males will be diagnosed with prostate cancer. Longer survivorship horizons and resource constraints are leading to increasing strain on current models of specialist-led follow-up care delivery. Nurse-led digital health innovations can reduce demand on specialists if co-designed with health care professionals (HCPs). Methods: Using human-centred design, the purpose of this exploratory qualitative study with ten HCPs was to characterize their experiences with prostate cancer (PCa) follow-up and virtual care to optimally situate a nurse-led clinic within this ecosystem. Results: HCPs highlight how current follow-up care can be fragmented, so the scope of a nurse-led clinic should focus on the management of care with referral as needed. Despite an ambivalence for telemedicine arising from its implementation during the COVID-19 pandemic, HCPs see potential for improved care through digital health but note several concerns. Burn-out, weakened patient-provider relationships, and creeping scope of responsibilities were identified as pain points. We provide a health ecosystem readiness checklist to improve odds of acceptability, appropriateness, and feasibility synthesized from six HCP-defined facilitators mapped onto Proctor’s outcomes. Facilitators include: functionality testing (acceptability), technical support and usability (adoption), expectation management (appropriateness), resource allocation (cost), staff readiness (feasibility), and staff training (penetration). Conclusion: Intentional and empathetic co-design with HCPs during the development of digital health innovations, such as digital therapeutics, is necessary to ensure that HCPs can benefit from, instead of being burdened from, the rising tide of digital health in their clinical responsibilities.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".