CMHSU: An R Statistical Software Package to Detect Mental Health Status, Substance Use Status, and Their Concurrent Status in the North American Healthcare Administrative Databases
Bibliographic record
Abstract
The concept of concurrent mental health and substance use (MHSU) status and its detection in patients has garnered growing interest among psychiatrists and healthcare policymakers over the past four decades. Researchers have proposed various diagnostic methods, including the Data-Driven Diagnostic Method (DDDM), for the identification of MHSU. However, the absence of a standalone statistical software package to facilitate DDDM for large healthcare administrative databases has remained a significant gap. This paper introduces the R statistical software package CMHSU (version 0.0.6.9), available on the Comprehensive R Archive Network (CRAN), for the diagnosis of mental health (MH) status, substance use (SU) status, and their concurrent (MHSU) status. The package implements DDDM using hospital and medical service physician visit counts along with maximum time span parameters for MH, SU, and MHSU diagnoses. A simulated real-world dataset incorporating fentanyl is presented to examine various analytical aspects, including three key dimensions of MHSU detection based on the DDDM framework, as well as temporal analysis to demonstrate the package’s application for healthcare policymakers. Additionally, the limitations of the CMHSU package and potential directions for its future extension are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".