Supporting Clinical Informatics Competency Development Among the Clinical Informatics Team at Providence Health Care
Bibliographic record
Abstract
Clinical informatics (CI) competencies are crucial for health care organizations to effectively use information communication technologies (ICTs) and deliver quality care. An interdisciplinary CI team can assist organizations with leveraging ICTs, but may also require support. This case study describes a peer-led knowledge translation project designed, delivered and implemented over two years by members of the CI team at Providence Health Care (PHC). The project included CI competencies assessment of CI team members, followed by tailored education for identified knowledge gaps. The Kirkpatrick evaluation model was used to assess three levels of learning among CI team members, including a satisfaction survey, pre-and post-cognitive retention of the education intervention using a validated tool for informatics specialists, and project partner feedback of CI team performance 12 weeks after education completion. This case study provides evidence-informed guidance on 'how to' implement peer-led, practice-based CI training for CI teams.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| 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".