29B. Enhancing Primary Healthcare Delivery in the Inner City through Interprofessional Team-based Care
Bibliographic record
Abstract
Focus Area: Sustainable Business Models This session will describe the evolution of an integrative model of primary healthcare delivery within a hospital-based setting. Over the past decade, the Academic Family Health Team (AFHT) in the Department of Family and Community Medicine at St Michael's Hospital in Toronto has been in the business of transforming primary healthcare. The AFHT provides services to the inner-city community of Toronto through 5 clinical sites located conveniently in the downtown east core. Our model of care incorporates patient- and family-centered values; interprofessional collaboration; and accessible evidence-based care and research. Evaluation of our efforts has proved emancipating for both clinicians and patients—with the delivery of integrative healthcare services, improved coordination of care, enhanced patient outcomes and satisfaction, improved quality of work life for our health professional team, and patient/family empowerment in self-help strategies. This model of care was identified by the Council of the Federation Health Care Innovation Working Group as one of 4 innovative models of primary care delivery to be emulated in Canada. The session will describe our experiences in building our integrative healthcare team. Facilitators and barriers to the development of successful integrative models of primary care will be described. Specific strategies that have facilitated success in our experience also will be shared.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".