Implementation of Symptom Protocols for Nurses Providing Telephone‐Based Cancer Symptom Management: A Comparative Case Study
Bibliographic record
Abstract
BACKGROUND: The pan-Canadian Oncology Symptom Triage and Remote Support (COSTaRS) team developed 13 evidence-informed protocols for symptom management. AIM: To build an effective and sustainable approach for implementing the COSTaRS protocols for nurses providing telephone-based symptom support to cancer patients. METHODS: A comparative case study was guided by the Knowledge to Action Framework. Three cases were created for three Canadian oncology programs that have nurses providing telephone support. Teams of researchers and knowledge users: (a) assessed barriers and facilitators influencing protocol use, (b) adapted protocols for local use, (c) intervened to address barriers, (d) monitored use, and (e) assessed barriers and facilitators influencing sustained use. Analysis was within and across cases. RESULTS: At baseline, >85% nurses rated protocols positively but barriers were identified (64-80% needed training). Patients and families identified similar barriers and thought protocols would enhance consistency among nurses teaching self-management. Twenty-two COSTaRS workshops reached 85% to 97% of targeted nurses (N = 119). Nurses felt more confident with symptom management and using the COSTaRS protocols (p < .01). Protocol adaptations addressed barriers (e.g., health records approval, creating pocket versions, distributing with telephone messages). Chart audits revealed that protocols used were documented for 11% to 47% of patient calls. Sustained use requires organizational alignment and ongoing leadership support. LINKING EVIDENCE TO ACTION: Protocol uptake was similar to trials that have evaluated tailored interventions to improve professional practice by overcoming identified barriers. Collaborating with knowledge users facilitated interpretation of findings, aided protocol adaptation, and supported implementation. Protocol implementation in nursing requires a tailored approach. A multifaceted intervention approach increased nurses' use of evidence-informed protocols during telephone calls with patients about symptoms. Training and other interventions improved nurses' confidence with using COSTaRS protocols and their uptake was evident in some documented telephone calls. Protocols could be adapted for use by patients and nurses globally.
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 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 | 0.034 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".