NURSE AND ORGANIZATIONAL CONTEXT CONSIDERATIONS FOR KNOWLEDGE USE IN PAIN CARE: A GUIDING FRAMEWORK
Bibliographic record
Abstract
Nurses are involved in many of the painful procedures performed on hospitalized children. In collaboration with physicians, nurses have an exceptional responsibility to have knowledge to manage the pain; however, the evidence indicates this is not being done. Issues may be twofold: opportunities to improve knowledge of better pain care practices and/or ability to use knowledge. Empirical evidence is available that, if used by health care providers, can reduce pain in hospitalized children. Theory guided interventions are necessary to focus resources designated for learning and knowledge translation initiatives in the area of pain care. Objective This paper presents the Knowledge Use in Pain Care (KUPC) conceptual model that blends concepts from the fields of knowledge utilization and work life context, including human resource management which are believed to influence the translation of knowledge to practice. The four main components in the KUPC model include those related to the organization, the individual nurse, the individual patient and the sociopolitical context. The KUPC model was conceptualized to account for the complex circumstances surrounding nurse’s knowledge uptake and use in the context of pain care. Conclusion The model provides a framework for healthcare administrators, clinical leaders and researchers to consider as they decide how to intervene to increase knowledge use to reduce painful experiences of children in hospital.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| 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".