An Implementation History of Primary Health Care Transformation: Alberta’s Primary Care Networks and the People, Time and Culture of change
Bibliographic record
Abstract
Abstract Background: Primary care, and its transformation into Primary Health Care (PHC), hasbecome an area of intense policy interest around the world. As part of this trendAlberta, Canada, has implemented Primary Care Networks (PCNs). These aredecentralized organizations, mandated with supporting the delivery of PHC, fundedthrough capitation, and operating as partnerships between the province’s healthcareadministration system and family physicians. This paper provides an implementationhistory of the PCNs, giving a detailed account of how people, time, and culturehave interacted to implement bottom up, incremental change in a predominantly Fee-For-Service (FFS) environment.Methods: Our implementation history is built out of an analysis of policy documentsand qualitative interviews. We conducted an interpretive analysis of relevant policydocuments (n=20) published since the first PCN was established. We then grounded12 semi-structured interviews in that initial policy analysis. These interviews explored11 key stakeholders’ perceptions of PHC transformation in Alberta generally, and theformation and evolution of the PCNs specifically. The data from the policy review andthe interviews were coded inductively, with participants checking our emerginganalyses. Results: Over time, the PCNs have shifted from an initial Frontier Era thatemphasized local solutions to local problems and featured few rules, to a present Eraof Accountability that features central demands for standardized measures,governance, and co-planning with other elements of the health system. A core groupof people – clinician and administration leaders – emerged to create the PCNs and,over time , to develop a long-term Quality Improvement (QI) vision and governanceplan for them as organizations. The continuing willingness of both these groups towork at understanding and aligning one another’s cultures to achieve thetransformation towards PHC has been central to the PCNs’ survival and success.Conclusions: Generalizable lessons from the implementation history of this emergingpolicy experiment include: The need for flexibility within a broad commitment toimproving quality. The importance of time for individuals and organizations to learnabout: quality improvement; one another’s cultures; and how best to support thetransformation of a system while delivering care locally.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.020 | 0.017 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".