Improving Rural Mental Health Service Quality Through Partnerships and Innovation
Bibliographic record
Abstract
In rural areas, poverty, geographic isolation, cultural differences, limited availability of providers, and other barriers can prevent the engagement and retention of clients. As a result, individuals living in rural areas often enter care later in the course of their illness with more serious symptoms, and require more intensive services. The Big Island Substance Abuse Council (BISAC) has attempted to reduce the effects of these barriers by implementing several innovative, agency-wide quality improvement efforts within a five-year span from 2012-2017: (a) a research partnership with a local university, (b) prioritization of leveraging information technology and electronic health records for a wide array of decision-making, (c) rebranding and grass roots marketing, (d) cultural competence in service delivery, and (e) routinized training and supervision. The methods by which these initiatives have developed within a rural behavioral health setting offer both suggestions and optimism for the proliferation of similar approaches elsewhere. This paper provides an account of BISAC’s infrastructure and program improvements, and illuminates several thematic lessons learned across implementation efforts. As described here, such innovations might provide clues for utilizing data to help guide decision making, integrating cultural values, and monitoring operations within rural mental health settings. Keywords: substance use treatment, community, program development, rural, program improvement, implementation
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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 | 0.031 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".