Using a Complex Systems Perspective to Achieve Sustainable Health Care Practice Change
Bibliographic record
Abstract
Background: There has been a surge of interventions at health care settings to achieve practice change, but sustaining these new practices remains challenging. The purpose of the study is to use the Legacy Sustainability Model, a framework grounded in complexity science, to examine the implementation and sustainability of an interprofessional (IP) collaboration intervention in health care. The model considers the six factors communication, connections, coherence, continuous assessment, commitment and constructs essential to building capacity for sustainability.Methods and Findings: Three health care settings in Alberta implemented IP practice interventions over a six-month period. After three and six months, we interviewed participants at each site about the progress of the IP intervention and emerging challenges. We examined the interview data for emergence of the six factors of the Legacy Sustainability Model. Conclusions: Our analysis showed distinct contextual differences between the three sites as represented by the strengths of the six factors at the outset of the IP interventions and the way the factors evolved throughout the project. Using a complex systems lens may be valuable for examining contextual factors that might affect the success of a practice intervention and for monitoring progress towards capacity building for lasting practice 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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".