Understanding integrated mental health care in “real-world” primary care settings: What matters to health care providers and clients for evaluation and improvement?
Bibliographic record
Abstract
PURPOSE: The integration of mental health specialists into primary care has been widely advocated to deliver evidence-based mental health care to a defined population while improving access, clinical outcomes, and cost efficiency. Integrated care has been infrequently and inconsistently translated into real-world settings; as a result, the key individual components of effective integrated care remain unclear. METHOD: This article reports findings from a qualitative study that explored provider and client experiences of integrated care. We conducted in-depth interviews with integrated care providers (n = 13) and clients (n = 9) to understand their perspectives and experiences of integrated care including recommended areas for quality measurement and improvement. The authors used qualitative content and reflexive thematic analytic approaches to synthesize the interview data. RESULTS: Clients and integrated care providers agreed regarding the overarching concepts of the what, how, and why of integrated care including co-location of care; continuity of care; team composition and functioning; client centeredness; and comprehensive care for individuals and populations. Providers and clients proposed a number of dimensions that could be the focus for quality measurement and evaluation, illuminating what is needed for successful context-sensitive spreading and scaling of integrated care interventions. CONCLUSION: With a mounting gap between the empirical support for integrated care approaches and the implementation of these models, there is a need to clarify the aims of integrated care and the key ingredients required for widespread implementation outside of research settings. This study has important implications for future integrated care research, and health care provider and client engagement in the quality movement. (PsycINFO Database Record
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".