MétaCan
Menu
Back to cohort
Record W2511267757 · doi:10.1155/2016/4250109

Improving the Treatment and Assessment of Moderate and Severe Pain in a Pediatric Emergency Department

2016· article· en· W2511267757 on OpenAlexaff
Roger Chafe, Debbie O. A. Harnum, Robert Porter

Bibliographic record

VenuePain Research and Management · 2016
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsJaneway Children's Health and Rehabilitation CentreMemorial University of Newfoundland
Fundersnot available
KeywordsEmergency departmentPain managementFocus groupMedicinePain assessmentPhysical therapyAcute painMEDLINENursing

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.361
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2016
Admission routes1
Has abstractyes

Explore more

Same venuePain Research and ManagementSame topicPediatric Pain Management TechniquesFrench-language works237,207