The e-Network Solution for Mental Health and Addictions Information Management
Bibliographic record
Abstract
Abstract ConnexOntario Health Services Information’s genesis was over 18 years ago. Back then, it was known as the Drug and Alcohol Registry of Treatment (DART). At that time, it was – and remains today – an innovative initiative that acted as an e-network solution for mental health and addiction information management. Using state-of-the-art technology and professional information management standards, DART was designed to offer a form of electronic healthcare by way of resource matching and referral for those who were seeking treatment for substance abuse problems. DART was also designed as a means to help improve the alcohol and drug treatment system in Ontario by providing easily accessible, up-to-date and accurate data about the availability of those services. This paper explores the development and growth of DART through its metamorphosis into ConnexOntario. Acting as a hub to the electronic network, the computerized database housing the Registry information provides the platform upon which information is shared amongst service providers, professionals, planners, government officials and members of the general public. Background In May of 1991, the Ontario Ministry of Health and Long-Term Care (MOHLTC) (formerly the Ministry of Health) requested that the Centre for Addiction and Mental Health (CAMH) (formerly the Addiction Research Foundation) develop a Registry of treatment services for drug and/or alcohol problems in Ontario. This was to be a three-year demonstration project. (For a more detailed description of the DART program, see Rush, Vincent and Chevendra [1993]). The rationale for this initiative, as provided by Rush and Chevendra (1994), included a number of identified needs for easily obtainable, reliable information about the availability and type of alcohol and drug treatment throughout Ontario. In 1990, Ontario residents were using out-of-country treatment services at an escalating rate, costing the Ontario Health Insurance Plan (OHIP) close to $50 million annually (Rush et al. 1993). Questions arose as to whether Ontario’s treatment system was being underutilized and/or whether the extensive use of the American treatment system indicated a shortage of specific types of treatment in Ontario. Rush and Chevendra (1994) referenced reviews by Martin (1990) and Mammolitti (1991) regarding an issue where members of the public and Case Study
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.017 |
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".