An examination of questioning methods and the influence of child maltreatment on paediatric pain assessments: Perspectives of healthcare providers
Bibliographic record
Abstract
AIMS AND OBJECTIVES: Children with a history of maltreatment have underestimated and undertreated pain; however, it is unknown if healthcare providers consider maltreatment when assessing children's pain. The current study aimed to address this issue by investigating healthcare providers' pain assessment practices, and specifically, their consideration of child maltreatment. METHOD: Healthcare providers (N = 100) completed a survey, asking them to reflect upon their pediatric pain assessment practices (e.g., methods and questions used to assess pain) through self-report and case vignette questions. RESULTS: Participants who received continuing education about child maltreatment were more likely to consider maltreatment in several areas of their pediatric pain assessment practice, whereas participants who received continuing education about pediatric pain, were not. Participants were also more likely to report that they would consider maltreatment in vignette responses than in questions regarding their daily practice. CONCLUSION: Findings indicate healthcare providers use multidimensional methods when assessing children's pain, although it is unclear when or how they use open-ended vs. option posing questions. Healthcare providers also tended to consider the effects of child maltreatment on children's ability to communicate their pain more so when the history of maltreatment was known to them.
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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 | 0.056 | 0.140 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".