Bibliographic record
Abstract
This paper reports a component of a larger study, Informatics: enhancing the Clinical Experience? (ICE), which explored the impact on the therapeutic relationship of the implementation and use of Electronic Medical Records (EMR) in British Columbia, Canada. As anticipated, EMRs were found to negatively affect the relationship in many clinics. However, surprisingly paper-based clinics were as likely as EMR-based clinics to report problems with maintaining eye contact with their patients. This led to an interesting finding; that as a result of this difficulty few family care providers actually chart when their patients are with them, preferring to build rapport and chart at a later time. Consequently three recommendations are made: 1) Improve medical education in the area of charting (paper & EMR-based) with the patient present; 2) Explore the affect different technologies and skills have on the ability of providers to chart with the patient present and 3) Develop an understanding that unless the technology and training improve Canadian family medicine will never gain the asserted benefits of EMRs, and that other incentives are needed if Canada is to meet its target of delivering Electronic Health Records (EHR) to 100% of all Canadians by 2015.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.093 | 0.043 |
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".