Impact of the clinical nurse specialist role for the myeloproliferative neoplasm program: Part Two – The team and patient care experiences
Bibliographic record
Abstract
Myeloproliferative neoplasms (MPN) are a group of rare clonal disorders of hematopoietic progenitor cells associated with disease- related symptoms, thrombotic events, and risk of transformation to acute myeloid leukemia (Tefferi, 2021). Their relative rarity and complexity of care led to the establishment of the MPN program at the Princess Margaret (PM) Cancer Centre, Toronto, Canada. The MPN program utilizes a shared-care model wherein partnering with local hematologists (shared-care partners) ensures that patients have access to a MPN specialist while continuing to receive care close to home. The clinical nurse specialist (CNS) role was implemented in late 2016 to support not only the shared-care model, but also to triage new patient referrals, and support consultation and follow-up. Although the CNS role has been part of the healthcare system since the 1940s, the role and its impact remain unclear at times to the inter-disciplinary team. This paper describes the process and results in evaluating the CNS role's impact in the MPN program through using a multi-method approach. In this Part II of a series, the focus is on discussing the team and patient care experience with having a CNS as part of the care team.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".