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Record W2810934466 · doi:10.7202/1048892ar

Évaluation du Plan d’action en santé mentale (2005-2015) : intégration et performance des réseaux de services

2018· article· fr· W2810934466 on OpenAlexafffundvenueabout
Marie‐Josée Fleury, Guy Grenier, Jean-Marie Bamvita, Catherine Vallée, Lambert Farand, François Chiocchio

Bibliographic record

VenueSanté mentale au Québec · 2018
Typearticle
Languagefr
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsDouglas CollegeMcGill University
FundersFonds de Recherche du Québec - Santé
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

Objectives This study aimed to: 1) assess implementation of the 2005-2015 Quebec mental health (MH) reform, and its enabling and hindering factors as well as MH team performance, in 11 local health service networks; then, for a subset of 4 networks: 2) identify processes influencing service quality in MH teams, and 3) analyze effects of team structures and processes on outcomes for service users.Methods The networks were selected in consultation with 20 MH decision makers. Data sources included: 1) documentation on population, organization and service characteristics, integration strategies, and network challenges; 2) individual and group interviews with 102 regional managers, MH professionals and managers from primary care or specialized MH teams, community organization directors, respondent psychiatrists and general practitioners (GPs); and 3) questionnaires completed by 16 respondent psychiatrists, 90 managers, 315 MH professionals from primary care or specialized teams, and 327 service users.Results Objectives of the MH reform were only partially achieved across the 11 health service networks, given the limited availability of practice guidelines related to implementing new structures and services, and reluctance among MH professionals (mainly GPs) to adopt them. As well, most primary care teams lacked GPs or psychiatrists. Implementation was more successful in large networks with specialized services located in general hospitals. The use of clinical tools and approaches, and frequent interactions with other teams or organizations enhanced team performance. Several team process variables including autonomy, involvement in decision-making, and knowledge sharing were strongly associated with the performance of MH professionals and higher quality services. While geographic variables (e.g. frequency of interactions with GPs) had more influence on performance in specialized services, individual variables (e.g. lower seniority in the team) and organizational variables (e.g. lower proportion of service users with personality disorders) influenced performance in primary care teams. Work satisfaction was more strongly associated with team process variables (e.g. fewer conflicts, higher team support, greater collaboration) and recovery-oriented services with organizational variables (e.g. primary care team). Some types of organizational culture were strongly associated with team performance (clan and hierarchical cultures), and work satisfaction (market culture). Concerning effects of team structure and processes on service user outcomes, higher quality of life and recovery scores were strongly associated with continuity and diversity of services. Finally, high seriousness of needs among service users represented a major obstacle for MH services attempting to address their quality of life issues and recovery.Conclusion This study suggests various measures that may improve MH service quality: promotion of more results-oriented organizational cultures, and greater collaboration, professional training on evidence-based practices, greater support for professionals, increasing their autonomy and involvement in decision-making, and more formalized integration strategies. Diversified and continuous biopsychosocial support was also recommended for improving quality of life and recovery among service users.

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)0.062
metaresearch head score (Gemma)0.073
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.902
Threshold uncertainty score0.711

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0620.073
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.006
Science and technology studies0.0020.002
Scholarly communication0.0050.002
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.127
GPT teacher head0.402
Teacher spread0.275 · 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 designObservational
Domainnot available
GenreEmpirical

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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Citations9
Published2018
Admission routes4
Has abstractyes

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