Implementing Key Strategies for Successful Network Integration in the Quebec Substance-Use Disorders Programme
Bibliographic record
Abstract
BACKGROUND: Fragmentation and lack of coordination often occur among organisations offering treatment for individuals with substance-use disorders. Better integration from a system perspective within a network of organisations offering substance-use disorder services can be developed using various integration strategies at the administrative and clinical levels. This study aims to identify integration strategies implemented in Quebec substance-use disorder networks and to assess their strengths and limitations. METHODS: A total of 105 stakeholders representing two regions and four local substance-use disorder networks participated in focus groups or individual interviews. Thematic qualitative and descriptive quantitative analyses were conducted. RESULTS: Six types of service integration strategies have been implemented to varying degrees in substance-use disorder networks. They are: 1) coordination activities-governance, 2) primary-care consolidation models, 3) information and monitoring management tools, 4) service coordination strategies, 5) clinical evaluation tools and 6) training activities. CONCLUSION: Important investments have been made in Quebec for the training and assessment of individuals with substance-use disorders, particularly in terms of support for emergency room liaison teams and the introduction of standardised clinical evaluation tools. However, the development of integration strategies was insufficient to ensure the implementation of successful networks. Planning, consolidation of primary care for substance-use disorders and systematic implementation of various clinical and administrative integration strategies are needed in order to ensure a better continuum of care for individuals with substance-use disorders.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".