Can we measure that? Review of quality indicators for person-centred and recovery-oriented mental health care in primary care
Bibliographic record
Abstract
Context: Promoting the integration of mental health services within primary care and community settings is a priority in Canada and internationally. It is important to ensure that such mental health services are accessible to the population but also essential that these services be as person-centred and recovery-oriented as possible. Quality indicators are needed to objectively assess the extent to which current mental health services are person-centred and recovery-oriented, and improve these practices over time. Objective: We aimed to identify indicators measuring person-centred and recovery-oriented care approaches for people with mental disorders in primary care and community-based settings. Study design and analysis: We performed an overview of systematic reviews and grey literature searches to identify eligible indicators. Published systematic reviews were identified through searches in Medline, Embase, CINAHL and PsycINFO (search period: 2009-2019). Reviews were eligible if they reported at least one indicator of person-centred or recovery-oriented care for mental disorders in primary care or community settings. For the gray literature search, we searched the websites and publications of 11 Canadian organizations and 14 international organizations that produce mental health quality indicators. Our descriptive and narrative analysis was guided by conceptual frameworks for person-centred care and recovery. Setting: Primary care and community settings. Population studied: Youth or adults with mental health or substance use conditions. Results: Our searches enabled us to identify 104 relevant indicators related to person-centred or recovery-oriented mental health care, including 32 reported in six systematic reviews and 72 available from 14 Canadian or international organizations. There were 89 indicators of person-centred care, with continuity of care and patient education being the most common sub-dimensions covered. Only 15 indicators of recovery-oriented care were identified. Limitations to the current set of indicators include a lack of specificity (in terms of population or care setting) and sources of data that remain underexploited. Conclusion: The current set of quality indicators represents a solid foundation to embed measurement of person-centred, recovery-oriented mental health care in health systems but the development of additional indicators linked to a broader range of sub-dimensions is urgently needed.
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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 | 0.087 | 0.325 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.025 | 0.035 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".