Technology for advancing behavioral health integration: implications for behavioral health practice and policy
Bibliographic record
Abstract
Behavioral health integration (BHI) encompasses the integration of general health, mental health, and substance use care. BHI has promise for healthcare improvement, yet several challenges limit its uptake and successful implementation. Translational Behavioral Medicine published the Continuum-Based Framework by Goldman et al., 2020 to create comprehensive guidance for BHI within primary care settings. Technology can help advance BHI and provide evidence to support it. This commentary describes challenges and illustrative use cases in which technology solutions help organizations achieve BHI through the Continuum-Based Framework domains. Two rounds of semi-structured interviews with field leaders, practice sites, and technology stakeholders identified key barriers in BHI amenable to technology solutions, applications of technologies, and how they facilitate BHI. Findings showed that technology can facilitate the implementation and scaling of BHI by reducing care fragmentation and improving patient engagement, accountability and financial sustainability, provider experience and support, and equitable access to culturally competent care. Continued efforts by stakeholders to address legacy policy and implementation issues (e.g. incentives, investment, privacy, and workforce) are needed to optimize the impact of technology on BHI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.079 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.017 | 0.029 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.024 | 0.022 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".