Optimizing Registered Nurse Roles in the Delivery of Cancer Survivorship Care within Primary Care Settings
Bibliographic record
Abstract
To address increasing pressures for cancer survivorship care (CSC), provincial cancer agencies have introduced new models of post-treatment follow-up involving earlier transition of cancer survivors back to primary care (PC) providers. It is unknown how nurses in PC settings have responded to this practice change. The purpose of this qualitative descriptive study was to examine registered nurses' (RNs) perspectives of the strengths, gaps, barriers and opportunities for optimizing nursing roles in the delivery of CSC within PC settings. Participants completed a demographical questionnaire and semi-structured, in-depth telephone interview. Data collection and analysis were conducted concurrently. Data were analyzed using content analysis approaches. The sample included 18 RNs working in diverse PC settings across Ontario. Participants' involvement in CSC was limited, but it could be categorized into the following three themes: care coordination and system navigation, emotional support and facilitating access to community resources. Individual, practice setting and PC team factors influenced nurses' involvement in CSC. To the best of our knowledge, this is the first Canadian study to examine RN roles in PC settings related to CSC. There is wide variability and opportunity to enhance RNs' roles and involvement in CSC.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".