Physician Perceptions About Generic Drugs
Bibliographic record
Abstract
BACKGROUND: With constrained health-care resources, there is a need to understand barriers to cost-effective medication use. OBJECTIVE: To study physician perceptions about generic medications. METHODS: Physicians used 5-point Likert scales to report perceptions about cost-related medication nonadherence, the efficacy and quality of generic medications, preferences for generic use, and the implications of dispensing medication samples. Descriptive statistics were used to assess physician perceptions and logistic regression models were used to evaluate predictors of physician perceptions. RESULTS: Among the invited sample, 839 (30.4%) responded and 506 (18.3%) were eligible and included in the final study population. Over 23% of physicians surveyed expressed negative perceptions about efficacy of generic drugs, almost 50% reported negative perceptions about quality of generic medications, and more than one quarter do not prefer to use generics as first-line medications for themselves or for their family. Physicians over the age of 55 years were 3.3 times more likely to report negative perceptions about generic quality, 5.8 times more likely to report that they would not use generics themselves, and 7.5 times more likely to state that they would not recommend generics for family members (p < 0.05 for all). Physicians reported that pharmaceutical company representatives are the most common (75%) source of information about market entry of a generic medication. Almost half of the respondents expressed concern that free samples may adversely affect subsequent affordability, yet two thirds of respondents provide free samples. CONCLUSIONS: A meaningful proportion of physicians expressed negative perceptions about generic medications, representing a potential barrier to generic use. Payors and policymakers trying to encourage generic use may consider educational campaigns targeting older physicians.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".