Understanding effective care management implementation in primary care: a macrocognition perspective analysis
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
BACKGROUND: Care management in primary care can be effective in helping patients with chronic disease improve their health status. Primary care practices, however, are often challenged with its implementation. Incorporating care management involves more than a simple physical process redesign to existing clinical care routines. It involves changes to who is working with patients, and consequently such things as who is making decisions, who is sharing patient information, and how. Studying the range of such changes in "knowledge work" during implementation requires a perspective and tools designed to do so. We used the macrocognition perspective, which is designed to understand how individuals think in dynamic, messy real-world environments such as care management implementation. To do so, we used cognitive task analysis to understand implementation in terms of such thinking as decision making, knowledge, and communication. METHODS: Data collection involved semi-structured interviews and observations at baseline and at approximately 9 months into implementation at five practices in one physician-owned administratively connected group of practices in the state of Michigan, USA. Practices were intervention participants in a larger trial of chronic care model implementation. Data were transcribed, qualitatively coded and analyzed, initially using an editing approach and then a template approach with macrocognition as a guiding framework. RESULTS: Seventy-four interviews and five observations were completed. There were differences in implementation success across the practices, and these differences in implementation success were well explained by macrocognition. Practices that used more macrocognition functions and used them more often were also more successful in care management implementation. CONCLUSIONS: Although care management can introduce many new changes into the delivery of primary care clinical practice, implementing it successfully as a new complex intervention is possible. Macrocognition is a useful perspective for illuminating the elements that facilitate new complex interventions with a view to addressing them during implementation planning.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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