Evaluating the Implementation of Integrated Mental Health Care: A Systematic Review to Guide the Development of Quality Measures
Bibliographic record
Abstract
OBJECTIVE: Although the effectiveness of integrated mental health care has been demonstrated, its implementation in real-world settings is highly variable, may not conform to evidence-based practice, and has rarely been evaluated. Quality indicators can guide improvements in integrated care implementation. However, the literature on indicators for this purpose is limited. This article reports findings from a systematic review of existing measures by which to evaluate integrated care models in primary care settings. METHODS: Bibliographic databases and gray literature sources, including academic conference proceedings, were searched to July 2014. Measures used or proposed to evaluate integrated care implementation or outcomes were extracted and critically appraised. A qualitative synthesis was conducted to generate a panel of unique measures and to group these measures into broad domains and specific dimensions of integrated care program performance. RESULTS: From 172 literature sources, 1,255 measures were extracted, which were distilled into 148 unique measures. Existing literature frequently reports integrated care program effectiveness vis-à-vis evidence-based care processes and individual clinical outcomes, as well as efficiency (cost-effectiveness) and client satisfaction. No measures of safety of care and few measures of equitability, accessibility, or timeliness of care were located, despite the known benefits of integrated care in several of these areas. CONCLUSIONS: To realize the potential for quality measurement to improve integrated care implementation, future measures will need to incorporate domains of quality that are presently unaddressed; microprocesses of care that influence effectiveness, sustainability, and transferability of models of care; and client and health care provider perspectives on meaningful measures of quality.
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 | 0.164 | 0.406 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.016 |
| Bibliometrics | 0.037 | 0.035 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".