Impact of an Educational Program on Pain Management in Patients With Cancer Living at Home
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To assess the effect of an educational homecare program on pain relief in patients with advanced cancer. DESIGN: Quasi-experimental (pretest post-test, nonequivalent group). SETTING: Four community-based primary care centers providing social and healthcare services in the Quebec City region of Canada. SAMPLE: 80 homecare patients with advanced cancer who were free of cognitive impairment, who presented with pain or were taking analgesics to relieve pain, and who had a life expectancy of six weeks or longer. METHODS: The educational intervention included information regarding pain assessment and monitoring using a daily pain diary and the provision of specific recommendations in case of loss of pain control. Pain intensity data were collected prior to the intervention, and reassessments were made two and four weeks later. Data on beliefs were collected at baseline and two weeks. All data were collected by personal interviews. MAIN RESEARCH VARIABLES: Patients beliefs about the use of opioids; average and maximum pain intensities. FINDINGS: Patients beliefs regarding the use of opioids were modified successfully following the educational intervention. Average pain was unaffected in the control group and was reduced significantly in patients who received the educational program. The reduction remained after controlling for patients initial beliefs. Maximum pain decreased significantly over time in both the experimental and control groups. CONCLUSIONS: An educational intervention can be effective in improving the monitoring and relief of pain in patients with cancer living at home. IMPLICATIONS FOR NURSING: Homecare nurses can be trained to effectively administer the educational program during their regular homecare visits.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".