Effect of health service integration on the health care use of patients with alcohol use disorders in North Karelia Finland 2016-2020: A comparative cross-sectional study
Bibliographic record
Abstract
Background: In North Karelia, Finland, a comprehensive integration of health and social services was implemented in 2017. This study sought to evaluate the impact of integration on the utilisation of health services among patients with alcohol use disorders (AUDs). Methods: Data from 2016 to 2020 were gathered from the electronic health records, encompassing both primary and specialised care, for patients with AUDs (n = 4344). Patients were identified based on AUD-related International Statistical Classification of Diseases and Related Health Problems (ICD-10) diagnosis codes. The data included information on the type of contact, reason for contact (ICD-10 code), and professional providing the service. Results: The proportion of patients with any annual contact with health services was approximately 90%, and this proportion remained unaffected by the integration. Decreases in AUD contacts were noted across the entire patient cohort, except for those diagnosed with AUD already in 2016. Emergency care use increased among patients treated in substance abuse services after the integration of services. Remote online and telehealth contacts increased across service domains, but these changes were unrelated to the integration year. Conclusion: The decrease in AUD contacts may be attributed to the improved identification of patients with less severe conditions, as the recording of diagnosis codes has improved. However, notable unmet care needs continue to exist.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".