Implementing a Substance Use Screening Protocol in Rural Federally Qualified Health Centers
Bibliographic record
Abstract
INTRODUCTION: In 2018, nearly 20% of Americans aged 12 years and older reported using illicit substances, with higher rates in rural areas. Federally Qualified Health Centers (FQHCs) provide health care to one in five rural Americans. However, estimates suggest that only 13.6% of patients in rural FQHCs receive substance use (SU) screening compared with 42.6% of patients in urban FQHCs. AIMS: This quality improvement (QI) project aimed to improve patient quality and safety and meet Health Resources and Services Administration reporting requirements. These aims were achieved through the design and implementation of a new SU screening protocol in four FQHCs in rural Indiana. METHOD: Deming's plan-do-study-act model was used to implement QI interventions to increase SU screening rates. A new SU screening tool, the National Institute on Drug Abuse -Modified Alcohol, Smoking, and Substance Involvement Screening Testwas implemented, and staff were trained on its use. the screening, brief intervention, and referral to treatment model was used as a guiding framework. Outcome measures included a comparison of SU screening rates from the first quarter of 2019 to the first quarter of 2020, as well a pretest-posttest designed to measure staff knowledge and attitudes regarding SU. RESULTS: Baseline SU screening rate in 2019 was 0.87%. This increased to 24.8% by March 2020. Additionally, posttest results demonstrated improvement from staff on all indices, and an approval rating of 77% of the new SU screening practices. CONCLUSIONS: This project demonstrated that a low-cost QI intervention can increase SU screening rates in rural FQHCs, as well as improve staff knowledge and attitudes regarding SU.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".