The Oncology Clinical Nurse Specialist: A Rapid Review of Implementation Models and Barriers around the World
Bibliographic record
Abstract
The role of a clinical nurse specialist in oncology varies greatly between healthcare systems, and implementing this healthcare role with its multifaceted and co-existing responsibilities may prove challenging. While already integrated into healthcare systems and services in several European countries, Asia, Canada, and the United States, other countries are just beginning to develop clinical nursing specialties. The current study aims to provide healthcare policymakers with up-to-date evidence that focuses on the diverse modes of oncology clinical nurse specialist role implementation across several healthcare systems and pertinent implementation challenges as described in the literature. A rapid evidence assessment was carried out in order to provide policymakers with a rigorous review in a condensed timescale. Initially, only items in the English language were included, and "grey literature" was excluded. We searched PubMed between 1 January 2022 and 28 February 2022 and two independent scholars reviewed items. Based on 64 papers, both non-scientific and papers that met the initial criteria of the rapid review, we describe the modes of implementation of the oncology clinical nurse specialist in the United States, Canada, United Kingdom, Japan, Brazil and Australia. Barriers to implementation include conflicts around role boundaries, skepticism and lack of organizational support, as well as fears that oncology clinical nurse specialists will "encroach" on doctors' powers. In contrast, an oncology clinical nurse specialist is found to be universally more accessible to patients and their families and can help physicians deal with difficult workloads, among other advantages. Conclusions: This role offers a myriad of gains for cancer patients, oncology physicians, and the healthcare system. The literature demonstrates that it is a necessary role, albeit one that brings specific implementation challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".