Standardizing Nursing Information in Canada for Inclusion in Electronic Health Records: C-HOBIC
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it