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Record W4362638607 · doi:10.1177/13674935231167965

Child maltreatment and pediatric pain: A survey of healthcare professionals’ pain knowledge and pain management techniques

2023· article· en· W4362638607 on OpenAlexafffund
Sarah Campbell, Matthew Baker, Kelly McWilliams, Shanna Williams

Bibliographic record

VenueJournal of Child Health Care · 2023
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsMcGill University
FundersMcGill University
KeywordsPain managementHealth professionalsMedicineHealth carePain assessmentFamily medicinePhysical therapy

Abstract

fetched live from OpenAlex

Children who have been maltreated are at an increased risk of having their pain under-recognized and undertreated by healthcare professionals, and thus, are more susceptible to adverse outcomes associated with undertreated pain. This study’s aims were to examine: ( 1) if healthcare professionals’ pediatric pain knowledge is associated with their pain assessment methods, ( 2) if maltreatment-specific pain knowledge is associated with consideration of child maltreatment when deciding on a pain management strategy, and ( 3) if pediatric pain knowledge would relate to maltreatment-specific pain knowledge. A sample ( N = 108) of healthcare professionals responded to a survey designed to examine their current knowledge and utilization of pediatric pain assessment and management with emphasis on the effects of child maltreatment. Findings revealed healthcare professionals’ knowledge of pediatric pain is independent of their pain assessment and management practices. However, general pain knowledge was associated with maltreatment-specific pain knowledge and generally, healthcare professionals were knowledgeable of child maltreatment’s impact on pediatric pain. Participants who considered a history of maltreatment were also more likely to employ sensitive questioning strategies when asking children about their pain.

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)0.019
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.657
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.342
Teacher spread0.323 · 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.

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

Citations3
Published2023
Admission routes2
Has abstractyes

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