Collaboration, culture and communication: Preparing the next generation to provide rural primary health care
Bibliographic record
Abstract
Background and objective: In an ever-changing landscape of health care needs and demands, the ability to provide care for rural communities is often overwhelming. Rural health care in a new decade demands targeted programs to improve recruitment, training, and sustained employment of primary care providers. This project served to address rural primary healthcare needs by the development of a project model to recruit, train, educate and evaluate Advanced Practice Registered Nurse students (APRN) students who were rigorously selected for a rural traineeship and practiced in rural counties. The evaluation of preceptors was also done to assist in retention and increased numbers of rural preceptors and clinical sites. This program was designed collaboratively and implemented with rural community partners and rural healthcare leaders.Methods: Graduate nursing students completed both a paper application and in person interviews to be selected for a rigorous 16-week clinical traineeship in the rural communities. Qualitative data were collected during interview and feedback sessions during their traineeship. Quantitative Data were collected on Process and Outcome Measures focused on learning objectives during their rural traineeship. These data were analyzed and evaluated to measure the effectiveness of program goals, outcomes, and sustainability of the program. Results and conclusions: The results support the structured process for selecting graduate students and with an innovative program design focused on rural culture and accessing resources for patients in these same rural areas. Both students and preceptors had improved performance and satisfaction over time. The results provide a road map to recreate programs with high clinical impact and increasing numbers of rural healthcare providers. Current follow-up data with APRN post program completion demonstrated increase in employment within rural areas post-graduation.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".