Child maltreatment and pediatric pain: A survey of healthcare professionals’ pain knowledge and pain management techniques
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".