Perceived factors influencing nurses' use of evidence-informed protocols for remote cancer treatment-related symptom management: A mixed methods study
Bibliographic record
Abstract
PURPOSE: To assess factors perceived to influence nurses' use of symptom protocols when providing remote management for oncology patients. METHOD: A mixed methods descriptive study was guided by the Knowledge-to-Action Framework. In 2013, 8 focus groups and 7 interviews were conducted with 49 nurses or patients/family members in three ambulatory oncology programs within different provincial healthcare systems. Role-play with a protocol was used during nurse focus groups/interviews. Nurses who provided remote symptom support received a survey. Data was triangulated using thematic analysis guided by the Ottawa Model of Research Use. RESULTS: Over 90% of nurses provide telephone support during regular hours only. These symptom protocols were being used by 14% of nurses at one program. Nurses rated the protocols positively for content and format (>85%) but 20% indicated too complex. Protocol facilitators were systematic approach, comprehensive, and evidence-based. Protocol barriers were too long, not for symptom clusters, and inadequate space for documenting. To facilitate use, nurses need to enhance their knowledge (73%) and skills (58%), get access to resources, and obtain performance feedback. Nurse barriers included the learning curve, being unaware of protocols, and feeling tied to a script. Organizational barriers were communication challenges with patients, lack of electronic charting, and no clear direction to use them (54%). CONCLUSIONS: Several barriers and facilitators were perceived to influence the use of symptom protocols. Nurses and patients/family members identified similar factors. Interventions are needed to overcome barriers to nurses using the protocols such as education, clear organizational mandate, and integration with documentation.
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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 | 0.040 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".