Differences in Team Mental Models Associated With Medical Home Transformation Success
Bibliographic record
Abstract
PURPOSE: Primary care transformation is widely seen as essential to improving patient outcomes and health care costs. The medical home model can achieve these ends, but dissemination and scale-up of practice transformation is challenging. We sought to understand how to move past successful pilot efforts by early adopters to widespread adoption by applying cognitive task analysis using the diffusion of innovations framework. METHODS: We undertook a qualitative cross-sectional comparison of 3 early adopter practices and 15 early majority practices in Alberta, Canada. Practices completed a total of 42 cognitive task analysis interviews. We conducted a framework-guided qualitative analysis, with allowance for emergent themes, using the macrocognition framework on which cognitive task analysis is based. Independent codings of interview transcripts for key macrocognitive functions were reviewed in group analysis meetings to describe macrocognitive functions and team mental models, and identify emergent themes. Two external focus groups provided support for these findings. RESULTS: Three prominent findings emerged. The first was a spectrum of mental models from "doctor with helpers," through degrees of delegation, to fully team based care. The second was differences in how teams distributed macrocognitive functions among members, with early adopters distributing these functions more widely across the team than early majority practices. Finally, we saw emergence of several themes also common in the diffusion of innovations literature, such as the importance of trying new practices in small, reversible steps. CONCLUSIONS: Our findings provide guidance to practice teams, health systems, and policymakers seeking to move beyond early adopters, to improve team functioning and advance the medical home transformation at scale.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".