Standardizing Nursing Information in Canada for Inclusion in Electronic Health Records: C-HOBIC
Bibliographic record
Abstract
The Canadian Health Outcomes for Better Information and Care (C-HOBIC) project introduced systematic use of standardized clinical nursing terminology for patient assessments. Implemented so far in three Canadian provinces, C-HOBIC comprises an innovative model for large-scale capture of standardized nursing-sensitive clinical outcomes data within electronic health records (EHRs). To support this activity, nursing assessment and outcomes concepts were mapped to the International Classification for Nursing Practice (ICNP(R)). By comparing serial data on a patient across multiple time points, the C-HOBIC model can generate nursing-sensitive patient outcome reports. A principle benefit of the C-HOBIC model is that it provides nurses with information critical to planning for and evaluating patient care. Inclusion of nursing information in either provincial databases or EHRs in three Canadian provinces promotes continuity of patient care across sectors of the healthcare systems in those provinces and also facilitates aggregation and analysis by administrators and policy makers. The C-HOBIC model provides standardized, consistent, interoperable clinical information that reflects nursing practice throughout the Canadian healthcare System.
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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 | 0.018 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.021 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".