The clinical adoption meta-model: a temporal meta-model describing the clinical adoption of health information systems
Bibliographic record
Abstract
Health information systems (HISs) hold the promise to transform health care; however, their adoption is challenged. We have developed the Clinical Adoption Meta-Model (CAMM) to help describe processes and possible challenges with clinical adoption. The CAMM, developed through an action research study to evaluate a provincial HIS, is a temporal model with four dimensions: availability, use, behaviour changes, and outcome changes. Seven CAMM archetypes are described, illustrating classic trajectories of adoption of HISs over time. Each archetype includes an example from the literature. The CAMM and its archetypes can support HIS implementers, evaluators, learners, and researchers.
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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 | 0.040 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.007 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".