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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".