What factors do critical access hospital trustee/board members believe are important to recruitment of physicians and do they differ from hospital administrators and physicians
Bibliographic record
Abstract
Background: Rural hospitals continue to struggle to recruit physicians. Examining trustee/board member perceptions of their community’s strengths and challenges related to physician recruitment may provide insight on how to sustain an effective workforce in these facilities.Objective: The purpose of this study is to identify similarities and differences between critical access hospital (CAH) trustee/board members’ perspectives on factors important to physician recruitment compared to their hospital administrators and physicians practicing in their facilities.Methods: The CAH Community Apgar Questionnaire (CAH CAQ) was expanded to include trustee/board member participation in Iowa. Online survey methods were used to compile information from trustees/board members, hospital administrators and physician from participating CAHs recruited by the Iowa Hospital Association.Results: A total of 16 Iowa CAH communities participated in the project in 2015. There were 17 administrators, 39 physicians and 23 board members respondents for a total of 79 respondents. Significant differences were found between trustee/board members and hospital administrators ratings on CAH CAQ factors loan repayment and transfer arrangements. Trustee/board members and physicians showed significant differences on scores for the CAH CAQ class factor hospital/community support and on factor ratings for teaching, administration, hospital sponsored continuing medical education and welcome and recruitment programs.Discussion: This study has identified commonalities and differences in how rural hospital trustee/board members and the administrators and physicians who work at their facilities view community strengths related to physician recruitment. Analyzing and discussing the areas of consensus and differences of opinion could help develop more effective physician recruitment strategies for these communities.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".