A two-centre survey of caregiver perspectives on opioid use for children’s acute pain management
Bibliographic record
Abstract
Abstract Background Given the current opioid crisis, caregivers have mounting fears regarding the use of opioid medication in their children. We aimed to determine caregivers’ a) willingness to accept, b) reasons for refusing, and c) past experiences with opioids. Methods A novel electronic survey of caregivers of children aged 4 to 16 years who had an acute musculoskeletal injury and presented to two Canadian paediatric emergency departments (ED) (March to November 2017). Primary outcome was caregiver willingness to accept opioids for moderate pain for their children. Results Five hundred and seventeen caregivers participated; mean age was 40.9 (SD 7.1) years with 70.0% (362/517) mothers. Children included 62.2% (321/516) males with a mean age of 10.0 (SD 3.6) years. 49.6% of caregivers (254/512) reported willingness to accept opioids for ongoing moderate pain in the ED, while 37.1% (190/512) were ‘unsure’; 33.2% (170/512) of caregivers would accept opioids for at-home use, but 45.5% (233/512) were ‘unsure’. Caregivers’ primary concerns were side effects, overdose, addiction, and masking of diagnosis. Caregiver fear of addiction (odds ratio [OR] 1.12, 95% confidence interval [CI] 1.01 to 1.25) and side effects (OR 1.25, 95% CI 1.11 to 1.42) affected willingness to accept opioids in the emergency department; fears of addiction (OR 1.19, 95% CI 1.07 to 1.32), and overdose (OR 1.15, 95% CI 1.04 to 1.27) affected willingness to accept opioids for at-home use. Conclusions Only half of the caregivers would accept opioids for moderate pain, despite ongoing pain following nonopioid analgesics. Caregivers’ fears of addiction, side effects, overdose, and masking diagnosis may have influenced their responses. These findings are a first step in understanding caregiver analgesic decision making.
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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 | 0.001 | 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) | 0.002 | 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".