Knowledge Transfer and Improvement of Primary and Ambulatory Care for Patients with Anxiety
Bibliographic record
Abstract
OBJECTIVE: To summarize current evidence on the effectiveness of different knowledge transfer and change interventions for improving primary and ambulatory anxiety care to provide guidance to professionals and policy-makers in mental health care. METHOD: We searched electronic medical and psychological databases, conducted correspondence with authors, and checked reference lists. Studies examining the effectiveness of knowledge transfer and interventions targeted at improvement of the recognition or management of anxiety in primary and ambulatory health care settings were included. Methodological details and outcomes were independently extracted and checked by 2 reviewers. Where appropriate, data concerning the impact of interventions on symptoms of anxiety were pooled using metaanalytical procedures. RESULTS: We identified 24 studies that met our inclusion criteria. Seven professional-directed interventions and 17 organizational interventions (including patient-oriented interventions) were identified. The methodological quality of studies was variable. Professional-directed interventions only impact the process and outcome of care when embedded in some sort of organizational intervention. Metaanalysis (n = 8 studies) showed no effect of diverse organizational interventions on patients' anxiety symptoms (effect size, -0.08; 95% confidence interval, -0.31 to 0.15; P = 0.50). Collaborative care interventions proved to be the most effective organizational intervention strategies. Six studies reported economic results: 4 studies showed that intervention had a high probability of being cost-effective. CONCLUSIONS: Collaborative care seems to be very promising for improving primary and ambulatory care for anxiety. At the level of management and policy, the results of this review mandate the need to offer fair and reasonable reimbursement for collaborative care programs.
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 | 0.016 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".