Training Change Agents in CTA to Bring Health Care Transformation to Scale
Bibliographic record
Abstract
Primary care medical practice is in a period of transformational change. Practices have limited capacity to cope with this transformation. Thousands of practices require support, and any intervention must both scale to that level and be usable by practices with limited change capacity. Various organizations train practice facilitators (PFs) to help with this transformation. We developed a training program for PFs to learn the basics of cognitive task analysis (CTA) to analyze and advise practices and to help them transform by improving macrocognitive functions. The training program comprised preparatory readings and 14 hr of didactic sessions and guided exercises over 2 days. That preparation was followed by a three-interview progression under actual field conditions: seconding for an experienced lead interviewer, leading with an experience interviewer as second, and leading with another PF as second. The data collection, analysis, and reporting are highly structured, tailored to the constraints of primary care, and scalable. Early experience with practices in Alberta indicates the resulting CTA reports to have significant impact. PFs have spontaneously transferred their use of CTA skills to other areas of their facilitation work.
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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 | 0.024 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".