What influences physician opioid prescribing for children with acute pain?
Bibliographic record
Abstract
Background: Pain is one of the most common symptoms encountered in the healthcare system, and opioids are among the top three medications used to treat it. Understanding the reasoning behind physicians' opioid prescribing practices is vital to safe practice. The primary objective of our study was to describe pediatric emergency physicians' decision-making process when prescribing opioids for children's acute pain management. Methods: This study employed qualitative methodology, using one-on-one semi-structured interviews within a grounded theory analytic framework. We employed purposeful sampling to recruit pediatric emergency physicians from across Canada. Interviews were conducted by telephone (December 2019-January 2021). Transcript analysis occurred concurrently with data collection, supporting data saturation and theory development considerations. Results: Eleven interviews were completed with participants representing each of Canada's geographic regions. Nine major themes emerged: (1) practice setting and outpatient opioid use, (2) condition-specific considerations, (3) physician confidence in medical evidence, (4) pain assessment challenges, (5) patient and family perspectives, (6) opioid safety concerns, (7) personal biases and experiences, (8) personal practice context, and (9) the Opioid Crisis/media influence. Most clinicians felt that they limited opioid use to those who needed it most; all participants described challenges managing acute pain, emphasizing the need for accurate pain measurement and better guidelines, evidence-based data, and knowledge translation. Clinicians were more comfortable treating pain in the emergency department, compared to discharge prescribing. They recognized the importance of co-therapy with non-opioids and the need for opioid risk assessment when prescribing. A family centered approach was recognized as the goal of practice. Conclusion: Clinicians are less comfortable prescribing opioids to children for at-home use and find pain assessment and lack of clear guidelines to be barriers to pain care. Knowledge translation strategies for safer practice and optimal acute pain management could support responsible and judicious opioid use.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".