A qualitative study to identify factors influencing COXIB prescribed by family physicians for musculoskeletal disorders
Bibliographic record
Abstract
INTRODUCTION: Cyclo-oxygenase-2 inhibiting (COXIB) anti-inflammatories have been the drug class prescribed for a large number of cases of musculoskeletal (MSK) disorders in Canada over the past 5 years. The Alberta Improvements for MSK Disorders (AIMS) initiative sought to better understand the COXIB prescribing situation by funding several studies. The objective of this qualitative study was to determine the factors underlying primary care physicians' medication prescribing behaviour during an office visit for an MSK disorder, with particular emphasis on the prescribing of COXIBs. METHODS: The target respondents were Alberta primary care physicians chosen from a stratified random sample to meet a wide range of characteristics. Individual, semi-structured interviews were used to assess decision pathways in four real cases chosen by the physician. A total of 19 interviews were conducted and analysed using an analytic inductive approach. RESULTS: Factors judged as being important to decision pathways in relation to COXIB prescribing for MSK disease included safety, patient characteristics, affordability to patients, availability of samples, drug company marketing practices, habit formation, time contstraints, previous clinical experience of doctors and/or patient with certain drugs and doctors' perception of absolute versus relative risk. Interpretation. Most physicians preferentially prescribed COXIBs subsequent to a complicated, multifactorial, but essentially patient-centred, decision-making process.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".