Canadian Health Outcomes for Better Information and Care: Making the Value of Nursing Visible through the Use of Standardized Data
Bibliographic record
Abstract
Contemporary nursing practice is being increasingly supported through the use of information and communication technologies (ICT), particularly for the purposes of documenting clinical assessments, interventions and outcomes within and across clinical settings. With the advent of clinical information systems, the opportunities to generate new understandings and nursing knowledge abound but are hampered by the absence of data standards. In Canada, efforts to construct nursing documentation using standardized terminology and measures have been underway for several years. The Canadian Health Outcomes for Better Information and Care (C-HOBIC) initiative inaugurated standardization of nursing terminology in Canada and provides the primary focus of this chapter. Specifically, the authors provide an overview of the (a) evolution of C-HOBIC, (b) role of the Canadian Nurses Association in advancing the work, (c) mapping of the C-HOBIC dataset to SNOMED-CT and ICNP, (d) implementation across Canada including specific examples and (e) challenges and current and future opportunities. The current National Nursing Data Standards (NNDS) initiative is also discussed, highlighting relevant activities within the domains of nursing practice, administration, research, education and health policy.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".