Improving the Treatment and Assessment of Moderate and Severe Pain in a Pediatric Emergency Department
Bibliographic record
Abstract
Background.The Janeway Children’s Hospital previously enacted a number of measures to improve pain management for patients in its emergency department (ED). While improvements were demonstrated, rates for the timely assessment and treatment of pain remain below standards of care.Objectives.The study objectives are to investigate the impact of the previous attempts to improve the treatment of pain and to explore ways to further improve pain management in the ED.Methods.Key informant interviews and a focus group were conducted with nurses, physicians, and parents whose children were identified as having severe pain.Results.Interviews were conducted with 31 parents or children, 9 physicians, and 8 nurses. The focus group was attended by 15 nurses. Previous initiatives were viewed as improvements. Continued barriers include difficulties in accurately capturing the level of pain, issues in treating pain for specific types of patients, and inadequacy in addressing patients in severe pain.Conclusion.Changes in pain treatment protocols can result in positive impacts but are likely insufficient on their own to achieve desired standards of care. Consistent measurement and engagement with staff can identify additional opportunities for improving pain management within an ED setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".