Oncology telepractice orientation program.
Bibliographic record
Abstract
Telepractice in oncology is an evolving practice for nurses and cancer patients. Understanding the needs of our patients led the nursing leaders of a large academic teaching centre to undertake the challenge of creating a measurable and sustainable Telephone Readiness Assessment and Orientation Program. The experiences of patients and families regarding telephone interactions triggered the launch of Live Voice Answer, a centre-wide telepractice change. To support the transition from clinic practice to telepractice with the introduction of Live Voice Answer, an orientation program was created to lay the foundation for nurses to provide excellent standardized telepractice care. A Practice Training Process map was designed to depict three stages required to successfully complete the orientation: Telephone Readiness Assessment, Telephone Practice Training session, and Practical Application. The frameworks used to support the Practice Training Process includes COSTaRS, College of Nurses of Ontario Practice Guideline: Telepractice, Cancer Care Ontario: Oncology Nursing Telepractice Standard, Cancer Care Ontario - Symptom Management Guides as well as our organizational policies. Benner's Novice to Expert Theory is also embedded to guide the skill/competency development. The purpose of this manuscript is to describe the telephone practice orientation program and share lessons learned that have contributed to its current evolution.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.111 | 0.017 |
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".