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Record W4300778257

[Improving population mental health by integrating mental health care into primary care].

2017· article· en· W4300778257 on OpenAlexaffabout
Matthew Menear, Michel Gilbert, Marie‐Josée Fleury

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsInstitut Universitaire en Santé Mentale de QuébecDouglas Mental Health University InstituteUniversité LavalCentre hospitalier de l'Université Laval
Fundersnot available
KeywordsMental healthIntegrated careHealth careNursingPopulationDiversity (politics)Conceptual frameworkMedicinePsychologyPolitical sciencePsychiatrySociologyEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

Objective The objectives of this review were to identify and compare major international initiatives aiming to integrate mental health services in primary care and to summarize the lessons learned for similar integration efforts in the province of Quebec, Canada.Methods We conducted a narrative review of the literature guided by a conceptual framework drawn from the literature on integrated care. We identified relevant initiatives to support primary mental health care integration through Pubmed searches and through previous systematic reviews on this topic. We then selected those initiatives that provided sufficient details on their key characteristics, outcomes, and implementation issues (e.g. barriers, facilitators). We focused our analysis on large-scale initiatives as these offered the most potential for impacts on population mental health. This process resulted in the selection of 20 initiatives that were described in 153 articles and reports. Our synthesis was guided by our conceptual framework, which distinguishes between five types of integration, namely clinical, professional, organizational, systemic and functional integration.Results Of the 20 primary mental health care integration initiatives, 3 targeted youth, 14 targeted adults or multiple age groups, and 3 were targeted towards seniors. Most initiatives aimed to implement collaborative care models for common mental disorders in primary care. Other initiatives focused on co-locating mental health professionals in primary care, supporting the emergence of a diversity of integration projects led by community-based primary care practices, or the merger of primary care and mental health organizations. Most initiatives were based on clinical, professional and functional integration strategies. Across initiatives, a range of positive outcomes were reported, notably to the accessibility and quality of services, the satisfaction of patients and providers, the costs of services, and impacts on patients' health and quality of life. Integration initiatives encountered many common barriers to implementation. However, steps taken to properly prepare and execute the implementation process, as well as ensure the sustainability of initiatives, helped initiative leaders to overcome certain barriers. The lessons for Quebec include the need to continue to reinforce evidence-based models of collaborative mental health care in primary care and promote a culture of continuous quality improvement and a more widespread use of information technologies that can support integrated care.Conclusion This review shows that integrating mental health services into primary care is a complex process that depends on a variety of strategies occurring at multiple levels of the healthcare system. However, it is also a unifying process that holds much potential to significantly impact the mental health and well-being of populations.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.334
Threshold uncertainty score0.664

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.108
GPT teacher head0.446
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations1
Published2017
Admission routes2
Has abstractyes

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