Training Change Agents in CTA to Bring Health Care Transformation to Scale
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it