The Experience of Opioid Prescribing by Ontario Dentists
Bibliographic record
Abstract
Background: Opioid prescribing guidelines for Ontario dentists were released in 2015. Studies reported a pill volume reduction, but not a decrease in total prescriptions. We explored Ontario dentists’ opioid prescribing experiences. Methods: This qualitative study recruited Ontario general dentists through purposive sampling. Interviews were conducted, transcribed and analyzed using thematic analysis until data saturation was achieved. Results: The theme "It’s a judgement call” illustrated a tension between clinician- and patient-centred care approaches when prescribing opioids. Participants relied on prior knowledge and experiences due to the complex nature of predicting pain, and prescribed to prevent suffering and enhance positive experiences. Some preferences may be seen as risky behaviours. Conclusions: Opioid prescribing was influenced by multiple factors. Most participants prescribed only after recommending non-opioid analgesics for acute dental pain. However, “just in case" prescribing resulted in inappropriate prescriptions, which should be avoided. These findings may inform future research, educational and practical interventions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".