Family Physician attitudes about prescribing using a drug formulary
Bibliographic record
Abstract
BACKGROUND: Drug formularies have been created by third party payers to control prescription drug usage and manage costs. Physicians try to provide the best care for their patients. This research examines family physicians' attitudes regarding prescription reimbursement criteria, prescribing and advocacy for patients experiencing reimbursement barriers. METHODS: Focus groups were used to collect qualitative data on family physicians' prescribing decisions related to drug reimbursement guidelines. Forty-eight family physicians from four Ontario cities participated. Ethics approval for this study was received from the Hamilton Health Sciences/Faculty of Health Sciences Research Ethics Board at McMaster University. Four clinical scenarios were used to situate and initiate focus group discussions about prescribing decisions. Open-ended questions were used to probe physicians' experiences and attitudes and responses were audio recorded. NVivo software was used to assist in data analysis. RESULTS: Most physicians reported that drug reimbursement guidelines complicated their prescribing process and can require lengthy interpretation and advocacy for patients who require medication that is subject to reimbursement restrictions. CONCLUSION: Physicians do not generally see their role as being cost-containment monitors and observed that cumbersome reimbursement guidelines influence medication choice beyond the clinical needs of the patient, and produce unequal access to medication. They observed that frustration, discouragement, fatigue, and lack of appreciation can often contribute to family physicians' failure to advocate more for patients. Physicians argue cumbersome reimbursement regulations contribute to lower quality care and misuse of physicians' time increasing overall health care costs by adding unnecessary visits to family physicians, specialists, and emergency rooms.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".