Using Administrative Data to Measure Ambulatory Mental Health Service Provision in Primary Care
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: We sought to determine the accuracy of administrative data for identifying mental health service provision in primary care. STUDY DESIGN: This was a chart abstraction study measuring agreement between billing data and clinical data on the binary variable "mental health visit." Data were collected from the charts and billing records of 5 academic family practice clinics in Toronto, Ontario (1999 to 2000). Billing claims (n = 952) were selected from the billings for all visits by a stratified random sampling technique. A blinded data abstractor reviewed the clinical charts and assigned diagnostic codes for each patient visit associated with the selected claims. Any visit with at least 1 abstracted mental health diagnostic code was defined as a mental health visit. The test characteristics of 4 administrative measures of mental health service provision, based on different combinations of billing codes, were calculated. RESULTS: The accuracy of the administrative data was 86.8% when compared with clinical data. The sensitivity of the 4 administrative measures ranged from 22.3% to 80.7%. The specificity ranged from 97.0% to 99.5%. CONCLUSIONS: This is the first study to establish the performance of administrative data in measuring mental health service provision in a primary care setting. In our setting, broadly defined administrative measures of mental health have excellent specificity and adequate sensitivity for exploring and understanding mental health service utilization.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it