Hiring internationally educated nurses in hospitals: role of competition and resource availability
Bibliographic record
Abstract
Objective: This study used Porter’s Five Forces Model and the Resource Dependence Theory (RDT) to examine the association of competition and other market factors with the hospital’s decision to hire internationally educated nurses.Methods: A panel design was used comprising a national sample of nonfederal, acute care hospitals (n = 4,116) in the United States. Data, for the years 2008-2012, were derived from American Hospital Association’s Annual Survey and Area Health Resource File. Logistic regression with hospital random effects and state and year fixed effects was conducted to test the above mentioned association.Results: The study findings suggest that hospitals hire internationally educated nurses as a strategy to meet their staffing needs in more competitive and diverse markets. Moreover, hospitals that hire internationally educated nurses are system-affiliated, larger, and see more Medicare patients than those that do not hire them.Conclusions: Findings of this study could help health care managers to understand the influence of market factors on utilization of internationally educated nurses to fulfill their hospitals’ nurse staffing needs. Furthermore, the study findings can inform policymakers in crafting policies on the use of internationally educated nurses as a strategy to address nursing shortages.
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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 | 0.006 | 0.023 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".