Interprofessional Collaboration in Ontario’s Family Health Teams: A Review of the Literature
Bibliographic record
Abstract
Background: In Ontario, 200 interprofessional Family Health Teams (FHTs) have been established since 2005 to improve primary healthcare access, patient outcomes, and costs. High levels of interprofessional collaboration are important for team success; however, effective team functioning is difficult to achieve. FHTs are in their infancy, and little is known about the determinants that have influenced the quality of team collaboration or the outcomes that FHTs have achieved. The objective of this article is to examine current knowledge regarding FHT team functioning.Methods and Findings: A search of the literature resulted in eleven articles for final analysis, which were primarily qualitative in nature. A narrative synthesis of study findings was completed. A number of common challenges to interprofessional collaboration were identified. Nevertheless, patients and providers described improved healthcare access, greater satisfaction, and enhanced quality of healthcare using a FHT approach. Collaboration was fostered by effective leadership, communication, outcome evaluation, and training for both professionals and patients alike.Conclusions: Ontario FHTs have generated improvements in healthcare access and outcomes. Collaborative team functioning, while present, has not reached its full potential. Supportive public policy, education for patients and providers, and evaluation research is needed to advance FHT functioning.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".