Addressing Educational Needs in Managing Complex Pain in Cancer Populations: Evaluation of APAM: An Online Educational Intervention for Nurses
Bibliographic record
Abstract
CONTEXT: Cancer-related pain is associated with significant suffering and is one of the most challenging symptoms to manage. Studies indicate that front-line clinicians often lack the knowledge on best practices in cancer pain management. OBJECTIVES: The current project, a quality improvement (QI) initiative, evaluated the outcome of an online educational intervention for nurses on complex cancer pain management. METHODS: An online 7-module educational intervention, Advanced Pain Assessment and Management, was offered from 2012 to 2017. Pre-post course evaluations included self-reported knowledge and confidence across cancer pain management domains. In-course competency assessments included knowledge examination, online discussion forum participation, opioid dosage calculation assignment, and small-group-based case study. A mixed-model statistical analysis was used to assess pre-post course change in pain management confidence level. RESULTS: In all, 306 nurses from 89 hospitals in Ontario, Canada, were enrolled in the course; 81.4% returned the precourse survey and 71.9% successfully completed the course. The average confidence level on pain management was low at baseline (57.5%) but improved significantly post-course. In-course competency assessments ranged from 81% to 89%. Mixed-model results showed post-course improvements in confidence levels, independent of sociodemographic background, clinical role, and professional educational level. Nurses with longer years of practice and more cancer cases reported greater confidence. CONCLUSION: A facilitator-led online educational intervention focusing on complex cancer pain management can significantly improve nurses' knowledge, confidence, and skills. Low baseline knowledge among nurses highlights the pressing need for health-care organizations to implement cancer pain management training as an integral part of health-care QI initiative.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".