Implementing Community Based Primary Healthcare for Older Adults with Complex Needs in Quebec, Ontario and New-Zealand: Describing Nine Cases
Bibliographic record
Abstract
The aim of this paper is to set the foundation for subsequent empirical studies of the "Implementing models of primary care for older adults with complex needs" project, by introducing and presenting a brief descriptive comparison of the nine case studies in Quebec, Ontario and New Zealand. Each case is described based on key dimensions of Rainbow model of Valentijn and al (2013) with a focus on "meso level" integration. Meso level integration is represented by organizational and professional elements of the Rainbow Model, which are of particular interest in our nine case studies. Each of the three cases in Ontario and three in New Zealand are different and described separately. In Quebec, a local health services network model is presented across the three cases studied with variations in the way it is implemented. The three cases selected in the three jurisdictions under study were not chosen to be representative of wider practice within each country, but rather represent interesting and unique models of community-based primary healthcare integration. Similarities and variations in the integrated care models, context and dimension of integration offer insights regarding core component of integration of services, offering a foundational understanding of the cases on which future analysis will be based.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".