Bibliographic record
Abstract
BACKGROUND: For all our successes, many urgent health problems persist, and although some of these problems may be explored with established research methods, others remain uniquely challenging to investigate-maybe even impossible to study in the real world because of practical and pragmatic obstacles inherent to the nature of the research question. OBJECTIVES: The purpose of this review article is to introduce agent-based modeling (ABM) and simulation and demonstrate its value and potential as a novel research method applied in nursing science. METHODS: An introduction to ABM and simulation is described. Examples of current research literature on the subject are provided. A case study example of community nursing and opioid dependence is presented. RESULTS: The use of ABM and simulation in human health research has increased dramatically over the past decade, and meaningful research is now commonly found published widely in respected, peer-reviewed journals. Absent from this list is innovative ABM and simulation research published by nurse researchers in nursing-specific journals. DISCUSSION: ABM and simulation is a powerful method with tremendous potential in nursing research. It is vital that nursing embrace and adopt innovative and advanced research methods if we are to remain a progressive voice in health research, practice, and policy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".