Evolving through Multiple, Co-Existing Pressures to Change: A Case Study of Self-Organization in Primary Care
Bibliographic record
Abstract
Context: Primary care clinics have experienced unprecedented changes to clinical and administrative routines to keep pace with changing pandemic requirements. Conceptualized as complex, adaptive systems, primary care clinics self-organize to maintain a workable balance. Self-organization varies and is not predictable; each clinic re-organizes routines by adapting its existing configurations of relationships among people, technologies, and material resources. Self-organization has proven hard to study, methodologically. Objective: Illuminate how a primary care clinic self-organizes over time in the face of multiple pressures, including those related to the COVID-19 pandemic. Study design/analysis: Virtual case study May – Nov 2021, including virtual meeting observations, document collection, interviews with clinic members, and brief weekly discussions to detect changes in clinical and administrative routines. Using schema analysis, and applying complexity theory and actor-network theory concepts, we described different adaptations chronologically, and then explored inter-relationships. We sought feedback on early results from the participating clinic (member checking). Setting: Mid-sized urban city in Canada. Population studied: Primary care clinic. Intervention/instrument: Semi-structured interview guide; field notes. Outcome measures: N/A. Results: The pandemic caused disequilibrium in 2020, where former clinical and administrative routines no longer sufficed. In 2021, the clinic continued to self-organize in the face of changing health policies, unintended consequences of earlier adaptations, and quality improvement initiatives. The clinic developed new feedback methods to detect emerging problems. Physical space, staffing, and technology were obvious influences on self-organization; changing one created ripple effects, sometimes generating new problems. Member checking confirmed we captured most of self-organization occurring during the case study period. Conclusions: The virtual case study illuminates the complex self-organization occurring in primary care over a year into the pandemic. Rather than expecting a return to ‘normal’, primary care clinics should be approached as changing entities with unique people-technology-resources relationships that are under continual revision. The findings support reflection by clinics on their own processes, as well as for external bodies who support or seek further change within primary care.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| 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".