UPDATING WAYS AND ENHANCING PAIN MANAGEMENT EFFICIENCIES AMONG HEALTHCARE
Bibliographic record
Abstract
Introduction: Understanding of the current landscape of interventions is important to enhance pain management competencies among healthcare professionals. This systematic review aimed to assess the prevalence and efficacy of interventions targeted at enhancing pain management competencies among healthcare professionals, with the goal of informing evidence-based practices and facilitating improvements in patient care. Methods: A comprehensive search strategy, tailored to electronic databases including PubMed, MEDLINE, Embase, and the Cochrane Library, was employed to systematically identify relevant studies until August 2023. Eligible studies, meeting specific criteria and published within the last 10 years, underwent a rigorous screening process, with inclusion based on primary research articles focused on pain management competencies among healthcare professionals. The subsequent data extraction and quality assessment, conducted by two independent reviewers, ensured a thorough and methodologically sound review of the literature. Results: The review included seven studies with diverse samples and interventions aimed at enhancing pain management competencies among healthcare professionals. The sample sizes varied from 82 to 358 participants, with an average improvement of 27% in sample knowledge representation. The interventions, spanning educational programs, simulation training, and collaborative strategies, demonstrated consistent effectiveness in improving knowledge and self-efficacy, with simulation training showcasing tangible improvements in practical skills and collaborative approaches leading to enhanced teamwork skills, quantified by a 40% improvement. The quantitative assessments revealed significant overall improvements, including a 23% increase in knowledge scores, a 28% rise in self-efficacy, and a 37% improvement in teamwork. Conclusions: Our study contributes to the existing literature by quantifying the substantial improvements observed in pain management competencies among healthcare professionals through diverse interventions, including educational programs, simulation training, and collaborative approaches, emphasizing the importance of a multifaceted strategy, with calculated odds ratios and percentages providing concrete measures for the development of evidence-based practices and educational strategies.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".