“I didn’t know it was an option”: Exploring caregivers’ knowledge of available pain management strategies in the paediatric emergency department
Bibliographic record
Abstract
Objectives: Caregivers, children, and healthcare professionals (HCPs) should engage in shared decision-making (SDM) regarding a child's pain and comfort care plan in the emergency department (ED). A better understanding of caregivers' current knowledge and gaps regarding pain and comfort care in the ED could inform conversations to support such SDM. Methods: This was a single-centre, cross-sectional survey with 341 participants recruited from April to July 2022. Survey questions included demographics, current knowledge and perceived gaps, as well as comfort (rated with a 4-point Likert scale) in advocating for pain management strategies. Results: A total of 345 caregivers responded to the survey; 77.7% (265/341) were mothers, and 84.6% (286/338) spoke English at home. About 45.6% (113/248) knew that children could receive pain medication at triage, and 42.3% (105/248) knew that children could receive numbing cream before a skin-breaking procedure; 42.7% (106/248) knew about the availability of food/drink. Participants reported similar mean (SD) comfort asking for pharmacologic [3.0 (0.9)] and non-pharmacologic [2.9 (1.0)] pain management strategies. Qualitative feedback from caregivers demonstrated SDM could be best achieved through having knowledge mobilization tools readily available and improved approachability of HCPs. Conclusions: While caregivers generally report feeling comfortable advocating for pain and comfort care for their children, many lacked knowledge of available options. Better educating caregivers in pain and comfort care options can support their agency to advocate for better care plans for their children.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".