New conceptual model of EMR implementation in interprofessional academic family medicine clinics.
Bibliographic record
Abstract
OBJECTIVE: To capture users' experiences with a newly implemented electronic medical record (EMR) in family medicine academic teaching clinics and to explore their perceptions of its use in clinical and teaching processes. DESIGN: Qualitative study using focus group discussions guided by semistructured questions. SETTING: Three family medicine academic teaching clinics in Winnipeg, Man. PARTICIPANTS: Faculty, residents, and support staff. METHODS: Focus group discussions were audiorecorded and transcribed. Data were analyzed by open coding, followed by development of consensus on a final coding strategy. We used this to independently code the data and analyze them to identify salient events and emergent themes. MAIN FINDINGS: We developed a conceptual model to reflect and summarize key themes that we identified from participant comments regarding EMR implementation and use in an academic setting. These included training and support, system design, information management, work flow, communication, and continuity. CONCLUSION: This is the first specific analysis of user experience with a newly implemented EMR in urban family medicine teaching clinics in Canada. The experiences of our participants with EMR implementation were similar to those reported in earlier investigations, but highlight organizational influences and integration strategies. Learning how to use and transitioning to EMRs has implications for clinical learners. This points to the need for further research to gain a more in-depth understanding of the effects of EMRs on the learning environment.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".