Development, successes, and potential pitfalls of multidisciplinary chronic disease management clinics in a family health team: a qualitative study
Bibliographic record
Abstract
BACKGROUND: The creation of Family Health Teams in Ontario was intended to reconfigure primary care services to better meet the needs of an aging population, an increasing proportion of which is affected by frailty and multimorbidity. However, evaluations of family health teams have yielded mixed results. METHODS: We conducted interviews with 22 health professionals affiliated or working with a well-established family health team in Southwest Ontario to understand how it approached the development of interprofessional chronic disease management programs, including successes and areas for improvement. RESULTS: Qualitative analysis of the transcripts identified two primary themes: [1] Interprofessional team building and [2] Inadvertent creation of silos. Within the first theme, two subthemes were identified: (a) collegial learning and (b) informal and electronic communication. CONCLUSION: Emphasis on collegiality among professionals, rather than on more traditional hierarchical relationships and common workspaces, created opportunities for better informal communication and shared learning and hence better care for patients. However, formal communication and process structures are required to optimize the deployment, engagement, and professional development of clinical resources to better support chronic disease management and to avoid internal care fragmentation for more complex patients with clustered chronic conditions.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".