Simulation Modeling for Psychiatric Service Planning: Protocol for a Mixed-Methods Study
Bibliographic record
Abstract
BACKGROUND: Mental health service managers must take into account multiple factors when making decisions about the best way to deliver care to clients across increasingly larger service areas. This task is made more difficult by the lack of evidence and tools historically available to inform these decisions. In recent decades, the increasing availability of epidemiological and service use data for mental illness has solved the problem of evidence, but there still exists a challenge to make these data easily accessible and understandable for managers. OBJECTIVE: This study aims to develop a simulation modeling tool to allow managers to explore various service configurations in virtual reality, enabling predictions to be made about the cost and quality of care. METHODS: This is a longitudinal, mixed-methods case study, comprising overlapping intervention and evaluation phases. In partnership with senior managers of a mental health program, the researchers will develop a series of simulation models in Arena to address key strategic issues facing the service. Thematic and content analyses of semistructured interviews, meeting observations, and document analysis will be used to evaluate the process of model implementation and the outcomes for both researchers and managers. The study is being conducted in Australia. RESULTS: Data collection has been ongoing since late 2013. To date, 3 prototype simulation models have been developed and presented to senior managers, and 18 evaluation interviews have been conducted. The project is expected to conclude in late 2018. CONCLUSIONS: Findings of this study have the potential to shape decision making in mental health service delivery, by providing key examples of how to integrate patient data using simulation modeling. In addition, the results will provide key insights into how researchers and consultants can effectively implement simulation modeling in real-world health care organizations. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/11119.
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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 | 0.090 | 0.111 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.112 | 0.013 |
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".