Where to Live and Work? Examining the Influence Of job and Community Characteristics On Post-graduation Plans among Physician Assistant Students
Bibliographic record
Abstract
Physician assistants are one way to address the healthcare provider shortage in rural areas. However, recruitment and retention of physician assistants to live and work in rural communities remain a challenge. One's relationship to his or her hometown, be it rural or not, has important implications for who might be willing to live and work in a rural community. The purpose of this research was to examine factors influencing physician assistant students’ desire to return to their hometowns to live and work after graduation. Data were collected in 2016 and 2017 through a survey administered to physician assistant students (n=149) in a PA program at a Midwestern University. Survey questions examined hometown connections, community features, and job expectations, as well as desire to return to one's hometown. Students who grew up in rural communities reported lower levels of community satisfaction and were less likely to perceive that jobs were available in their hometowns. A series of regression models were run to examine the impact of hometown connections, community characteristics and evaluation, and job characteristics in predicting interest in returning to hometown while controlling for the effects of age and gender. Coming from a rural, suburban, or urban hometown did not significantly predict desire to return when controlling for respondents' evaluation of their hometowns. The strongest predictor of willingness to return to one's hometown was community attachment. Understanding the factors that impact physician assistant students’ post-graduation plans may inform efforts to recruit and retain rural health care providers. Keywords: Physician assistants, rural health care, community attachment, rural youth, rural outmigration, health care shortages, medical provider recruitment
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".