Urine Clarity Inaccurate to Rule Out Urinary Tract Infection in Women
Bibliographic record
Abstract
PURPOSE We examined the perspectives of family physicians (FPs) on opioid prescribing and management of chronic pain to better understand the barriers to safer prescribing in primary care and differences in perspectives that may be potential drivers of practice variation. METHODS We used an exploratory qualitative study design. Semistructured interviews were conducted in June and July 2017 with 22 FPs in Ontario and coded inductively. Thematic analysis was used to identify themes, and a framework analysis explored the influence of physician demographics on prescribing experience. RESULTS Three key themes emerged: the discrepancy between FPs’ training and current practice, the tension between the FP’s role and patient and system expectations, and the influence of length of time in practice and strength of therapeutic relationships on perspectives on opioid prescribing. There was an overarching sentiment among participants that FPs are unsupported in their efforts to manage chronic pain. More years in practice (≥15 years) seems to influence practice patterns by increasing trust in therapeutic relationships and decreasing reliance on emergent guidelines (vs clinical experience). CONCLUSION Number of years in practice influences FPs’ response to emergent evidence, requiring initiatives to include strategies tailored to individual beliefs. Initiatives must move beyond dissemination and education to equip FPs with the skills they need to navigate emotionally charged conversations. External pressures and misaligned system and patient expectations place FPs at the center of a challenging situation, which may result in a higher risk of burnout compared with that of their specialist colleagues.
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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 | 0.006 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".