Knowledge, attitudes and practices of Canadian pediatric emergency physicians regarding short-term opioid use: a descriptive, cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: In the midst of the current opioid crisis, physicians are caught between balancing children's optimal pain management and the risks of opioid therapy. This study describes pediatric emergency physicians' practice patterns for prescribing, knowledge and attitudes regarding, and perceived barriers to and facilitators of short-term use of opioids. METHODS: We created a survey tool using published methodology guidelines and distributed it from October to December 2017 to all physicians in the Pediatric Emergency Research Canada database using Dillman's tailored design method for mixed-mode surveys. We performed bivariable binomial logistic regressions to ascertain the effects of clinically significant variables (e.g., training, age, sex, degree of worry regarding severe adverse events) on use of opioids as a first-line treatment for moderate pain in the emergency department, and prescription of opioids for moderate or severe pain for at-home use in children. RESULTS: Of the 224 physicians in the database, 136 (60.7%) completed the survey (60/111 [54.1%] women; median age 44 yr). Of the 136, 74 (54.4%) had subspecialty training. Intranasally administered fentanyl was the most commonly selected opioid for first-line treatment of moderate (47 respondents [34.6%]) and severe (82 [60.3%]) pain due to musculoskeletal injury. On a scale of 0 (not worried) to 100 (extremely worried), physicians' median score for worry regarding physical dependence was 6.0 (25th percentile 0.0, 75th percentile 16.0), for worry regarding addiction 10.0 (25th percentile 2.0, 75th percentile 20.0) and for worry regarding diversion of opioids 24.5 (25th percentile 14.0, 75th percentile 52.0). On a scale of 0 (not at all) to 100 (extremely), the median score for influence of the opioid crisis on willingness to prescribe opioids was 22.0 (25th percentile 8.0, 75th percentile 49.0). The top 3 reported barriers to prescribing opioids were parental reluctance (57 [41.9%]), lack of clear guidelines for pediatric opioid use (35 [25.7%]) and concern about adverse effects (33 [24.3%]). Binomial logistic regression did not identify any statistically significant variables affecting use of opioids in the emergency department or prescribed for use at home. INTERPRETATION: Emergency department physicians appeared minimally concerned about physical dependence, addiction risk and the current opioid crisis when prescribing opioids to children. Evidence-based development of guidelines and protocols for use of opioids in children may improve physicians' ability to manage pain in children responsibly and adequately.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".