Financial Incentive Required for Pharmacy Students to Accept a Post-Graduation Position in Rural and Undesirable Pharmacy Settings
Bibliographic record
Abstract
Background: It has been estimated that in 2018, 20% of pharmacy students were unemployed following graduation. However, many pharmacy positions go vacant each year, with the majority of these positions existing in rural areas. Methods: Pharmacy students completed a one-time, anonymous, online questionnaire. Measures of interest included: subject characteristics and preference in a variety job offers. Discrete Choice Experiment methodology of questionnaire design was used and Conditional Logit models were conducted to analyze the data to determine the financial incentive required for pharmacy students to take a post-graduate job with particular traits. Conclusions: A total of 283 students completed questionnaires from Iowa, North Dakota, South Dakota, Saskatchewan, and Manitoba. The majority of subjects were female, P3 students, and from a non-rural hometown. American students would need to be paid an additional $18,738 in salary to practice in a rural area, while Canadian students would require an additional $17,156. Canadian respondents would require an additional $7125 in salary to work in a community pharmacy with a low level of patient interaction compared to a community position with a large amount of patient interaction. Overall, pharmacy student preferences in post-graduation job attributes vary significantly between states and provinces.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".