A Quality Improvement Project on Pain Management at a Tertiary Pediatric Hospital
Bibliographic record
Abstract
To assess and improve pain management practices for hospitalized children in an urban tertiary pediatric teaching hospital.MethodsHealth Quality Ontario Quality Improvement (QI) framework informed this study. A pre (T1) – post (T2) intervention assessment included chart reviews and children/caregiver surveys to ascertain pain management practices. Information on self-reported pain intensity, painful procedures, pain treatment and satisfaction were obtained from children/caregivers. Documented pain assessment, pain scores, and pharmacological/non-pharmacological pain treatments were collected by chart review. T1 data was fed back to pediatric units to inform their decisions and pain management targets.ResultsAt T1, 51 (58% of eligible participants) children/caregivers participated. At T2, 86 (97%) chart reviews and 51 (54%) children/caregivers surveys were completed. Most children/caregivers at T1 (78%) and T2 (80%) reported moderate to severe pain during their hospitalization. A mean of 2.6 painful procedures were documented in the previous 24 h, with the most common being needle-related procedures at both T1 and T2. Pain management strategies were infrequently used during needle-related procedures at both time points.ConclusionNo improvements in pain management as measured by the T1 and T2 data occurred. Findings informed further pain management initiatives in the participating 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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".