Bibliographic record
Abstract
Ontario Health Teams (OHTs) are a newer model of intersectoral health care in Ontario, Canada designed to provide integrated care across a variety of sectors and organizations to a large attributed population. With the first cohort of OHTs being approved in 2019 and 4 cohorts being approved thereafter, each existing OHT is in a varying degree of maturity. Under the OHT model, community organizations coordinate with OHTs as a unified team to provide integrated and cohesive care with a focus on population health. This type of coordination invites the challenge of organizing and sharing data across organizational partners in a stream-lined way to deliver timely and evidence-based care. Two OHTs addressed this challenge by developing a data dictionary and repository to better integrate data flow and identify gaps in services to better serve all members of the communities. Based on the collective insights drawn from two OHTs, we present the results of a population health-based data development initiative. This process began with extensive engagement with partner organizations within each OHT and the collective governance teams, including patient and family advisory members. We then developed frameworks to begin asset mapping for data dictionary indicators to develop an understanding of what the current state was in the OHTs. After the indicator mapping was completed, key indicators that were relevant for the current priorities of the OHT as determined in a collective stakeholder process were identified to develop a dynamic and timely data repository within the OHTs. Significant learning has occurred with the development of the data dictionary and repository with the process encountering unexpected needs for education around data literacy and strategies to engage patients and collective governance structures. Because these projects were being engaged simultaneously in two different OHTs that varied by cohort and attributed population demographics, a comparison to identify common and differing enablers and challenges will be undertaken. Next steps once the data dictionary and repositories have been completed will be to evaluate the process and identify educational modules and frameworks that can be developed to promote data development and capacity within the OHTs in order to help them provide evidence-based integrated care to their communities. These resources will enable future collaboration, inform planning and decision-making, and identify existing and potential data gaps within the OHTs. Beyond the OHTs, understanding this process with the resulting frameworks and modules will be applicable across integrated care models of care that desire to build data literacy and capacity and engage in population health.
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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 | 0.087 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.017 | 0.028 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".