Managing symptoms during cancer treatments: evaluating the implementation of evidence-informed remote support protocols
Bibliographic record
Abstract
BACKGROUND: Management of cancer treatment-related symptoms is an important safety issue given that symptoms can become life-threatening and often occur when patients are at home. With funding from the Canadian Partnership Against Cancer, a pan-Canadian steering committee was established with representation from eight provinces to develop symptom protocols using a rigorous methodology (CAN-IMPLEMENT©). Each protocol is based on a systematic review of the literature to identify relevant clinical practice guidelines. Protocols were validated by cancer nurses from across Canada. The aim of this study is to build an effective and sustainable approach for implementing evidence-informed protocols for nurses to use when providing remote symptom assessment, triage, and guidance in self-management for patients experiencing symptoms while undergoing cancer treatments. METHODS: A prospective mixed-methods study design will be used. Guided by the Knowledge to Action Framework, the study will involve (a) establishing an advisory knowledge user team in each of three targeted settings; (b) assessing factors influencing nurses' use of protocols using interviews/focus groups and a standardized survey instrument; (c) adapting protocols for local use, ensuring fidelity of the content; (d) selecting intervention strategies to overcome known barriers and implementing the protocols; (e) conducting think-aloud usability testing; (f) evaluating protocol use and outcomes by conducting an audit of 100 randomly selected charts at each of the three settings; and (g) assessing satisfaction with remote support using symptom protocols and change in nurses' barriers to use using survey instruments. The primary outcome is sustained use of the protocols, defined as use in 75% of the calls. Descriptive analysis will be conducted for the barriers, use of protocols, and chart audit outcomes. Content analysis will be conducted on interviews/focus groups and usability testing with comparisons across settings. DISCUSSION: Given the importance of patient safety, patient-centered care, and delivery of quality services, learning how to effectively implement evidence-informed symptom protocols in oncology healthcare services is essential for ensuring safe, consistent, and effective care for individuals with cancer. This study is likely to have a significant contribution to the delivery of remote oncology services, as well as influence symptom management by patients at home.
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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 | 0.355 | 0.505 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".