Implementing Practice Guides to Improve Cancer Symptom Management in Homecare: A Comparative Case Study
Bibliographic record
Abstract
Symptoms experienced by clients with cancer often occur at home and can become life-threatening, posing serious safety concerns. This study evaluated implementation of evidence-informed symptom practice guides to enhance quality of cancer symptom support by homecare nurses. A comparative case study was conducted using the Knowledge to Action Framework. Case were created for each of six agencies providing nursing care within a regional homecare authority. A team of researchers and knowledge users (e.g., managers, educators, family member): (1) assessed factors influencing practice guide use (survey, interviews) at six agencies, (2) adapted 15 practice guides for local use, (3) implemented with interventions to address barriers, and (4) monitored use. Analysis was within and across cases. Of six nursing agencies, one withdrew and five participated. In the baseline survey, 51% of nurses reported using guidelines but nurses did not describe using them in their current practice during the interviews. To overcome barriers, 489 nurses in five agencies were trained in how to use the practice guides, principles were established for documenting cancer symptom management, and practice guides were made available in various formats. Success with implementation varied across the participating cases. Chart audits conducted in three agencies revealed evidence of practice guide use for 16%, 22%, and 70% client visits, respectively. Implementation of evidence-informed practice guides in nursing required an approach tailored for each agency. Training, integration in documentation, and easy access to practice guides increased use for some nurses but ongoing support and reinforcement from nurse leaders is required.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 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".