Health Service Information Leadership Connex <i>Ontario</i>: A Complete Information System Solution
Bibliographic record
Abstract
This article, a product of an IIMHL‐brokered partnership, concerns the requisites of today's health services information systems, and how an organisation in London, Ontario, Canada is responding to the addictions and mental health service information needs of the citizens of the Province of Ontario. The piece draws a parallel between theory regarding how stored data can be translated into information, knowledge, understanding and, ultimately, wisdom, and the practical needs of information and referral organisations as per their objective of providing their consumers with the value of current and accurate information. In the case of ConnexOntario ‐ funded by Ontario's Ministry of Health and Long‐Term Care ‐ the keys to this value are the powerful database that is used to house the data, and the innovative ‘front‐end application’ ‐ ConnexOntario eServices ‐ that allows users to input, retrieve and present the information as necessary. An emphasis is also placed on how eServices, in concert with the ConnexOntario database, helps promote the principle of mental health service leadership for its stakeholders, which is relevant as per the stated objective of the IIMHL.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.009 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".