A randomized controlled study to enhance nurses' pain management self-efficacy and knowledge through educational interventions
Bibliographic record
Abstract
Purpose: Nurses’ understanding of pain, commitment to education, and positive attitude are vital for providing high-quality pain care. The goal of this study was to find out how a pain management educational program (PMEP) influenced nurses' self-efficacy, knowledge, and attitudes toward pain care. Methods: A randomized experimental study design was utilized. The study was conducted in a governmental hospital in United Arab Emirates, with a total sample of 143 nurses (75 in the interventional group and 68 in the control). The interventional group received six-hours PMEP. Both groups completed questionnaires at baseline (T1), immediately after the program (T2), and at one month (T3). Results: In comparison to T1 (59.31%), the interventional group's Knowledge and Attitude Survey Regarding Pain (KASRP) scores increased significantly to 82.12% and 77.24% at T2 and T3, respectively. However, the control group exhibited no significant changes. From T1 (M = 2.93 ± 1.27) to T2 (M = 4.27 ± 0.68) and T3 (M = 4.21 ± 0.70), the overall scores of Pain Management Self-Efficacy Questionnaire (PMSEQ) rose notably among interventional group. With no significant increase in the PMSEQ scores of control group at T2 and T3. Positive correlations between KASRP and overall PMSEQ scores were observed in the interventional group at T1 and T2 (p < .05) but not at T3 (p = .120), whereas the control group showed significant correlations at all-time points (p < .001). Conclusions: The PMEP significantly enhanced nurses’ knowledge, attitudes, and self-efficacy in pain management. These improvements were sustained for one month post-PMEP, with positive correlations observed over time between KASRP and overall PMSEQ scores in both groups. Incorporating well-structured PMEP into nursing curricula is highly recommended. Further prospective research is recommended to support these findings.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".