Health Outcomes for Better Information and Care (HOBIC): Integrating Patient Outcome Information into Nursing Undergraduate Curricula
Bibliographic record
Abstract
Nursing-sensitive outcomes provide common information across sectors, thus eliminating duplication that frequently occurs as individuals move across settings. These outcomes also facilitate increased trust among colleagues and support common understandings of patient care needs, thus enhancing continuity of care. Outcomes-oriented information is also likely to increase patient safety and improve overall quality of care. Shared standards and data support consistent decision-making, as nursing decisions can be tracked back over time to assess patient care outcomes. Consequently, nurses will have the means to determine the impact of their interventions on patient outcomes. At the same time, adoption of common approaches to patient assessment leads to greater professional accountability and moves nursing care from a task orientation to an outcomes focus. For administrators, such improvements in monitoring and evaluating patient outcomes translate into improvements in efficiencies and effectiveness, thus providing a return on investment in implementing these outcomes within their agency. For nurses, integration and utilization of outcomes information increases the visibility and significance of their decision-making and patient care. Together with patients, nurses can utilize the outcomes information to make evidence-based decisions and advocate for appropriate care. At an aggregate level, the use of outcomes information creates a continuous feedback loop that is essential to ensuring evidence-based care and the best possible patient outcomes, not only for individuals, but also for families, communities and populations. Outcomes-oriented care provides a gateway for transforming the way we care for patients; puts safe, ethical, high-quality care for patients first; embodies the principles of evidence-based practice; ensures that the value of nursing is clearly understood within the larger system; and ensures that the requirements for measurability and accountability can be achieved. This journey is continuous and is being expanded to engage all other health disciplines in understanding and documenting their contributions to patient care, both as individual practitioners and as members of a healthcare team. Preparing nursing students in an outcomes approach will facilitate systemwide adoption of HOBIC patient outcomes over time and provide a means to determine the impact of nursing care on our patients.
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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 | 0.055 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".