Gaining knowledge of Ontario’s community mental health and addictions system: linking community-based health services data with administrative health data in Toronto, Ontario, Canada
Bibliographic record
Abstract
IntroductionIn most developed countries, a significant amount of mental health and addictions care occurs in community settings. Data reflecting populations served by community-based mental health and addictions providers and the types of services provided are not available, resulting in an incomplete reflection of the entire mental health and addictions system within existing administrative data. Objectives and ApproachThe Community Business Intelligence (CBI) initiative is a data collection project that captures information on adults receiving community-based mental health, addictions, and support services in Toronto Central Local Health Integration Network (LHIN), located in Ontario, Canada. Leveraging administrative health data and data linkage capacity at the Institute for Clinical Evaluative Sciences (ICES), along with engagement of external stakeholders knowledgeable of CBI and the community health sector, we linked the 2015/16 CBI dataset to administrative health data. Demographic characteristics, health-service utilization, primary care attachment, and 30-day emergency department (ED) revisits were calculated for individuals accessing community health services. ResultsThere was an 80.8% linkage rate, of which 36.9% linked deterministically via health card number, while 43.9% linked probabilistically. After study exclusions, 37,688 individuals in the CBI dataset used community health services between April 2015 and March 2016. Compared to Toronto Central LHIN, a greater proportion in the CBI dataset were female, older than 65 years of age, and living in a low income neighbourhood. Furthermore, 95.5%of individuals had at least one outpatient physician visit, 51.3%had at least one ED visit, and 21.7%had at least one hospitalization in the past year. Few individuals in the CBI dataset were without primary care attachment (4.5%); however, a larger proportion had a 30-day ED revisit, particularly those receiving community addictions services (19%). Conclusion/ImplicationsThe availability of community health services data in the CBI dataset and its successful linkage to the administrative health data held at ICES identified health service intersections and outcomes that were previously unknown. This linkage project demonstrates a successful framework for sector-wide performance measurement to address a critical infrastructure gap.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.019 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".