Managing Symptoms During Cancer Treatments: Barriers and Facilitators to Home Care Nurses Using Symptom Practice Guides
Bibliographic record
Abstract
Nurses are instrumental in helping clients safely manage at home and triage potentially life-threatening symptoms from cancer. The purpose of this study was to assess factors influencing home care nurses’ use of 15 evidence-informed symptom practice guides for providing telephone or in-home nursing services to clients with cancer. A mixed-methods descriptive study was guided by the Knowledge-to-Action Framework. All six nursing agencies within a regional home care authority participated. Data collection included retrospective audit of symptom management in 50 patient records, 14 interviews, and barriers survey from 150 of 243 (61.7%) registered nurses and registered practical nurses providing cancer symptom support in home care. Chart audit revealed more than 80% of clients were on chemotherapy and common symptoms were nausea/vomiting (44%), constipation (32%), fatigue (32%), loss of appetite (32%), and pain (20%). Nurses had positive intentions ( M = 5.4 out of 7; SD = 1.3) and felt capable of using the symptom practice guides ( M = 5.4; SD = 1.0), held strong beliefs about the consequences ( M = 5.8; SD = 1.1) and moral norms of using them ( M = 5.7; SD = 1.1), and identified neutral to low social influence ( M = 3.0; SD = 1.6). Common barriers were inadequate time in practice, learning curve, need to integrate into documentation, and competing system changes. Common facilitators were being comprehensive, an evidence-based resource for use in practice, and having consistent symptom management guides across settings. Overall, the symptom guides were well received by the nurses. Interventions nurses identified to overcome barriers were education, clear organizational mandate for implementation, and integration with documentation.
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 | 0.009 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".