Family Health Teams in Ontario – Vorstellung eines kanadischen Primärversorgungsmodells und Anregungen für Deutschland
Bibliographic record
Abstract
The German healthcare system is struggling with fragmentation of care in the face of an increasing shortage of general practitioners and allied health professionals, and the time-demanding healthcare needs of an aging, multimorbid patient population. Innovative interprofessional, intersectoral models of care are required to ensure adequate access to primary care across a variety of rural and urban settings into the foreseeable future. A team approach to care of the complex multimorbid patient population appears particularly suitable in attracting and retaining the next generation of healthcare professionals, including general practitioners. In 2014, the German Advisory Council on the Assessment of Developments in the Health Care System highlighted the importance of regional, integrated care with community-based primary care centres at its core, providing comprehensive, population-based, patient-centred primary care with adequate access to general practitioners for a given geographical area. Such centres exist already in Ontario, Canada; within Family Health Teams (FHT), family physicians work hand-in-hand with pharmacists, nurses, nurse practitioners, social workers, and other allied health professionals. In this article, the Canadian model of FHT will be introduced and we will discuss which components could be adapted to suit the German primary care system.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".