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Record W2763618338 · doi:10.1093/pch/19.6.e35-187

191: Pediatric Pain Management: the Emergency Medicine Residents' Perspectives

2014· article· en· W2763618338 on OpenAlexaffabout
Huma Fatima Ali, Christine Meyers, Janeva Kircher, Joe MacLellan, Sanaa N. Al‐Haj Ali

Bibliographic record

VenuePaediatrics & Child Health · 2014
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicinePediatric emergency medicinePain managementEmergency departmentAcute painPain assessmentFamily medicineCross-sectional studyEmergency medicinePhysical therapyMedical emergencyEmergency physicianPsychiatry

Abstract

fetched live from OpenAlex

Pain is a component of up to 80% of all emergency department (ED) visits. Oligoanalgesia, the under-treatment of pain, is a recognized problem in the ED and can have numerous detrimental effects for the pediatric patient. Surveying emergency medicine residents, our primary objectives were to describe a) the extent and type of training received in pediatric acute pain management, b) their management approach to common painful pediatric presentations, c) their level of comfort in assessing and treating acute pain, d) their perceived facilitators and barriers to optimally managing pain, and e) their attitudes towards managing such pain in the ED. A descriptive, cross-sectional survey was administered to all Royal College Emergency Medicine (EM) and Pediatric Emergency Medicine (PEM) residents, the Canadian College of Family Physician's EM residents, Clinical Fellows and PEM Fellows at three Canadian universities. A novel survey tool was created and distributed from May to June 2013. Surveys were completed online, using a modified Dillman approach. Our response rate was 46% (56 of 122). Forty-five percent (25 of 55) of residents had not received any training for pediatric pain assessment. All years of residents were uncomfortable with the assessment of pain in 0- to 2-year-olds (P=0.07). For patients aged two to 12 years, level of comfort with pain assessment increased with years of training (P=0.02). When assessing pain in children with disabilities, 83% (45 of 54) of residents were ‘extremely’ or ‘somewhat’ uncomfortable; 65% (47 of 55) of residents had not received any training on how to assess pain for such patients. Sixty-nine percent (38 of 55) of residents had received training on how to treat pediatric pain. All residents were more comfortable using pain medication for a nine-year-old, as compared to a one-year-old (oral oxycodone P=0.00003, oral morphine P=0.00002, IV morphine P=0.004). Ninety-eight percent (54 of 55) of residents felt that it was ‘extremely’ or ‘somewhat’ important to receive education about pediatric pain management. Their preferred methods to learn children's pain management were through role modeling (61%) and lectures (57%). Top challenges to optimal pediatric pain management were non-verbal patients, patients with disabilities, and young infants. Pediatric pain management education is important to EM residents. Residents recall receiving sub-optimal training on this important topic. They are especially uneasy in the management of younger children and those with disabilities. This study's findings can be used to inform post-graduate curriculum planning, in order to optimize pediatric pain assessment and treatment education.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.014
GPT teacher head0.299
Teacher spread0.285 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2014
Admission routes2
Has abstractyes

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