Bibliographic record
Abstract
Hospitals and other health settings across Canada are transitioning from paper or legacy information systems to Electronic Medical Records (EMR) systems to improve patient care and service delivery. The literature speaks to benefits of EMR systems, but also challenges, such as adverse patient events and provider workflow interruptions. Theoretical models have been proposed to help understand the complex interaction between health information technologies and the healthcare environment, but a shortcoming is the transition from conceptual models to actual clinical settings. The health ecosystem is filled with human diversity and organizational culture considerations that cannot be separated from technical implementation strategies. This paper analyzes literature on EMR implementation and adoption to develop a tactical framework for EMR adoption. The framework consists of six categories, each with a set of seed questions to consider when leading technology adoption projects.
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.
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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".