Creating Sustainable Change in the Interprofessional Academic Family Practice Setting: An Appreciative Inquiry Approach
Bibliographic record
Abstract
Background: There is a global shift toward integrated care approaches in primary care. Understanding how to optimize healthcare team effectiveness is of utmost interest in Canada, where primary care reform targets the development of interprofessional teams of providers collaborating to improve patient care. This article presents findings from a longitudinal study of one primary healthcare team in transformation. A theory-based organizational change model is applied to understanding the processes of change in interprofessional healthcare teams.Methods and Findings: We report findings from two years after the implementation of an intervention to advance teamwork in one family health team in Ontario. The intervention was informed by the Appreciative Inquiry (AI) approach. Fifty hours of unstructured clinic observations and interviews were conducted. The findings revealed that a change in team practice, such as patient-centredness, and formal and informal communication opportunities, precede change in team discourse—the way that members speak and think about themselves as an integrated team.Conclusions: The evolution of teamwork in the family practice setting is a gradual, steady process that begins with important changes in the way that things are done (i.e., first-order change), and with continued support and nurturance, can eventually lead to changes in the way that members think and speak about their team (i.e., second-order change).
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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 | 0.026 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.030 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| 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".