Toward Best Practice Guidelines and Curricula for Virtual Care (Telehealth) in a Cancer Center: Protocol for a Multimethod Study
Bibliographic record
Abstract
Background: Virtual health care, originally as telephone-based telehealth, has been used for more than 45 years; however, the literature shows limited understanding of the competencies required for safe virtual care practice in nursing and other health-related fields. This has led to a widening education-practice gap. Objective: The aim of this study is to identify (1) clinical guidelines for nurses and other health professionals undertaking routine virtual health (telehealth) assessment, triage, and follow-up care; and (2) curricula for preparing health professionals for virtual care. Subsequently, data will be collected within a major cancer treatment service to codetermine core competencies and curricula for nurses engaged in telehealth clinics. Methods: This was a phased multimethod study including reviews of existing literature, followed by qualitative (in-depth interviews, n=20) and quantitative (online survey, n=200) data collection and co-design workshops (n=5) to achieve project aims. Implementation will involve a pilot and an evaluation before full rollout of the developed guidelines and syllabus. Results: Literature reviews completed in the initial phase of this project confirm a paucity of existing guidelines for virtual health assessment and an urgent need to develop telehealth or virtual care competency frameworks and curricula for health professionals in training or practice. We propose an approach to develop and test these materials in practice. A total of US $52.6 was provided by a philanthropic alumni for support over the full duration of the project. Recruitment and collection from human participants will commence on February 1, 2026, following procurement of ethics clearance. Phase 2 data collection and analysis will occur from February to December 2026. Results will be presented to the ethics committee and clearance gained to implement and evaluate the program in 2027. Conclusions: Working with health providers, consumers, and academics toward standards of practice and curricula is clearly needed to ensure that the current and future nursing workforce is prepared for the continuing rise in virtual care. Completion of this project will fill an existing gap in the provision of guidelines and education for nurses providing 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 | 0.071 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.059 | 0.012 |
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".