Impact of team-based primary care on health care utilization among patients with mental and substance use disorders: a systematic review of English-language articles
Bibliographic record
Abstract
OBJECTIVE: To conduct a systematic review of the literature on the impact of team-based primary care on downstream health care utilization (all-cause or mental health-specific emergency department (ED) visits and hospitalizations) among people with mental or substance use disorders. METHODS: A literature search was conducted using the Scopus, MEDLINE, and Web of Science databases. Gray literature and forward and backward citation searches yielded additional results. Two independent reviewers screened the abstracts and full texts. Both reviewers performed a critical appraisal of the methodological quality using a modified Downs and Black checklist. The data were extracted using a standardized data extraction spreadsheet, and the effect sizes of studies were synthesized. RESULTS: A total of 18 studies were included (16 in the USA and 2 in Canada). Seven of the 15 studies that assessed the effect of team-based care on all-cause ED visits found they were associated with a lower number or odds of visits. Of the 15 studies that assessed the effect of team-based approaches on all-cause hospitalizations, 8 found that they were associated with an overall decrease. Very few studies assessed mental health-related ED visits (n = 2) or hospitalizations (n = 4), and the findings varied. All included studies were of fair quality (mean score ± standard deviation: 17.4 ± 1.3). CONCLUSION: Team-based care is likely associated with a decrease in all-cause ED visits and hospitalizations. A team-based primary care approach has the potential to reduce downstream healthcare utilization for patients with mental or substance use disorders and improve health outcomes.
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 | 0.014 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".