An Interprofessional Consensus of Core Competencies for Prelicensure Education in Pain Management: Curriculum Application for Nursing
Bibliographic record
Abstract
BACKGROUND: Ineffective assessment and management of pain is a significant problem. A gap in prelicensure health science program pain content has been identified for the improvement of pain care in the United States. METHOD: Through consensus processes, an expert panel of nurses, who participated in the interdisciplinary development of core competencies in pain management for prelicensure health professional education, developed recommendations to address the gap in nursing curricula. RESULTS: Challenges and incentives for implementation of pain competencies in nursing education are discussed, and specific recommendations for how to incorporate the competencies into entry-level nursing curricula are provided. CONCLUSION: Embedding pain management core competencies into prelicensure nursing education is crucial to ensure that nurses have the essential knowledge and skills to effectively manage pain and to serve as a foundation on which clinical practice skills can be later honed. [J Nurs Educ. 2015;54(6):317-327.].
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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 | 0.044 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".