Optimizing Virtual Follow-Up Care: Realist Evaluation of Experiences and Perspectives of Patients With Breast and Prostate Cancer
Bibliographic record
Abstract
BACKGROUND: Virtual follow-up (VFU) has the potential to enhance cancer survivorship care. However, a greater understanding is needed of how VFU can be optimized. OBJECTIVE: This study aims to examine how, for whom, and in what contexts VFU works for cancer survivorship care. METHODS: We conducted a realist evaluation of VFU among patients with breast cancer and prostate cancer at an urban cancer center during the COVID-19 pandemic. Realist evaluations examine how underlying causal processes of an intervention (mechanisms) in specific circumstances (contexts) interact to produce results (outcomes). Semistructured interviews were conducted with a purposive sample of patients ≤5 years after diagnosis. Interviews were audio-recorded and analyzed using a realist logic of analysis. RESULTS: Participants (N=24; n=12, 50% with breast cancer and n=12, 50% with prostate cancer) had an average age of 59.6 (SD 10.7) years. Most participants (20/24, 83%) were satisfied with VFU and wanted VFU options to continue after the COVID-19 pandemic. However, VFU impacted patient perceptions of the quality of their care, particularly in terms of its effectiveness and patient centeredness. Whether VFU worked well for patients depended on patient factors (eg, needs, psychosocial well-being, and technological competence), care provider factors (eg, socioemotional behaviors and technological competence), and virtual care system factors (eg, modality, functionality, usability, virtual process of care, and communication workflows). Key mechanisms that interacted with contexts to produce positive outcomes (eg, satisfaction) were visual cues, effective and empathetic communication, and a trusting relationship with their provider. CONCLUSIONS: Patients value VFU; however, VFU is not working as well as it could for patients. To optimize VFU, it is critical to consider contexts and mechanisms that impact patient perceptions of the patient centeredness and effectiveness of their care. Offering patients the choice of in-person, telephone, or video visits when possible, coupled with streamlined access to in-person care when required, is important. Prioritizing and addressing patient needs; enhancing physician virtual socioemotional behaviors and technology competency; and enhancing VFU functionality, usability, and processes of care and communication workflows will improve patient perceptions of the patient centeredness and effectiveness of virtual 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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| 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".