Pain Management Practices in a Pediatric Emergency Room (PAMPER) Study
Bibliographic record
Abstract
BACKGROUND AND AIM: Children's pain in emergency departments (EDs) is poorly managed by nurses, despite evidence that pain is one of the most commonly presenting complaints of children attending the ED. Our objectives were 2-fold: to verify if tailored educational interventions with emergency pediatric nurses would improve nurses' knowledge of pain management and nurses' pain management practices (documentation of pain, administration of analgesics, nonpharmacological interventions). METHODS: This intervention study with a pre-post design (baseline, immediately after the intervention [T-2], and 6 months after intervention [T-3]) used a sample of nurses (N = 50) and retrospective chart reviews of children (N = 450; 150 charts reviewed each at baseline, T-2, and T-3) who presented themselves in the ED with a diagnosis known to generate moderate to severe pain (burns, acute abdominal pain, deep lacerations, fracture, sprain). Principal outcomes: nurses' knowledge of pain management (Pediatric Nurses Knowledge and Attitudes Survey [PNKAS] on pain) and nurses' clinical practices of pain management (Pain Management Experience Evaluation [PMEE]). RESULTS: Response rate on the PNKAS was 84% (42/50) at baseline and 50% (21/42) at T-2. Mean scores on PNKAS were 28.2 (SD, 4.9; max, 42.0) at baseline and 31.0 (SD, 4.6) at T-2. Results from paired t test showed significant difference between both times (t = -3.129, P = 0.005). Nurses who participated in the capsules improved their documentation of pain from baseline (59.3%) to T-2 (80.8%; chi = 12.993, P < 0.001) as well as from baseline (59.3%) to T-3 (89.1%; chi = 29.436, P < 0.001). In addition, nurses increased their nonpharmacological interventions from baseline (16.7%) to T-3 (31.9%; chi = 8.623, P = 0.003). Finally, we obtained significant differences on pain documentation between the group of nurses who attended at least 1 capsule and the group of nurses who did not attend any capsule at both times (T-2 and T-3; chi = 20.424, P < 0.001; chi = 33.333, P < 0.001, respectively). CONCLUSIONS: The interventions contributed to the improvement of the nurses' knowledge of pain management and some of the practices over time. We believe that an intervention tailored to nurses' needs and schedule has more impact than just passive diffusion of educational content.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".