Implementing and Assessing the Benefits and Feasibility of a “Safe Sampling” Pilot Project
Bibliographic record
Abstract
Background: “Traditional” medication sampling occurs when pharmaceutical companies promote products by providing free samples to physicians, who then pass these on to patients. Limitations of this type of sampling include no pharmacist involvement, limited documentation on patients' medication profiles, limited inventory control in physicians' offices, and lack of patient-specific labeling. We present an improved method called “safe sampling” to mitigate these limitations. Objective: To implement a safe sampling pilot project and to assess its benefits and feasibility from the perspectives of patients, pharmacists, and pharmaceutical company employees. Methods: Physicians provided rabeprazole vouchers to patients in lieu of samples. Patients redeemed the vouchers at the pharmacy for a complimentary seven-day supply. The pharmacist processed the voucher like a prescription. Patients, pharmacists, and pharmaceutical company employees were surveyed after the intervention. Results: The pharmacy received 59 vouchers, which were redeemed by 43 patients and issued by 7 physicians (September 2004—January 2005). The pharmacy filled all vouchers. Of the 42 patients who completed the questionnaires, up to 95% agreed with the various benefits of safe sampling. All pharmacists ( n = 5) and pharmaceutical company employees ( n = 2) agreed that safe sampling was in the patient's best interest and was feasible to implement. Recommendations to enhance safe sampling included involving more than one pharmacy, dispensing medication from existing pharmacy inventory, using online billing, and determining who will pay for safe sampling. Conclusion: All participants acknowledged the benefits of safe sampling. Survey results suggest that pharmaceutical companies may not be willing to absorb the medication cost and/or professional fee involved in dispensing medication samples through pharmacies. Currently, pharmaceutical companies are paying for sample medications and their packaging and distribution. We suggest transferring these funds to the pharmacist's professional fee and sample medication costs. Our surveys suggest that safe sampling is beneficial to patients and is feasible to implement in community practice once the logistics of reimbursement are addressed. Safe sampling provides the groundwork for the creation of a safer, more sustainable method of medication sampling. Additional studies involving a larger patient population, more complex drugs, and a wider selection of pharmacies are needed to further quantify the benefits of safe sampling.
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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 | 0.106 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".