A call for nurse practitioner-led cancer survivorship clinics: The need for development and adoption within Ontario, Canada
Bibliographic record
Abstract
The growing prevalence of cancer survivors requiring comprehensive follow-up care after the completion of treatment is placing a significant strain on the Canadian healthcare system (Moura et al., 2022). Given the current landscape and the higher workload demands that are further exacerbated by shortages in healthcare staffing, the oncology specialist-led care, as the standard model for survivorship care is unsustainable and suboptimal in addressing a broad range of physical, psychosocial, supportive, informational, and rehabilitative needs of cancer survivors (Brennan et al., 2010; Canadian Partnership Against Cancer & Canadian Association of Provincial Cancer Agencies, 2010). Nurse-led models of survivorship care provided by oncology nurse practitioners (NPs) have been shown to be safe, effective, feasible, and appropriate for follow-up care (Chan et al., 2018). In the province of Ontario, survivorship care is provided mostly by physicians. Specialized NP-led survivorship clinics or programs are currently lacking based on a recent environmental scan. This paper outlines current barriers and opportunities in NP-led survivorship care. This is a call to action and for advocacy regarding the examination of oncology services and outlines the need for the development and implementation of NP-led survivorship clinics in Ontario, Canada.
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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 | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".