"The Potential is Unlimited!" Nurse Leader Perspectives on the Integration of HOBIC in Ontario
Bibliographic record
Abstract
Nurse leaders from a sample of acute care and long-term care sites participating in the Health Outcomes for Better Information and Care program in Ontario provided information on their experiences with HOBIC implementation. In addition, they described strategies to enhance successful implementation of the program. Finally, they discussed the potential future uses they envisioned for healthcare settings from the HOBIC data. Organizational benefits, such as data comparability, effective patient care planning and delivery and enhancement of nurses' technology skills were identified. Challenges that were highlighted included attaining buy-in from staff nurses, integration of HOBIC into existing computer systems and the subsequent computer and information technology challenges related to implementing such a program. Additional education and support for nursing staff and management were suggested as approaches for overcoming barriers. This survey demonstrates interest and commitment to HOBIC from many nurse leaders in Ontario and highlights the value that such a program provides for staff nurses in the planning and implementation of care. Nurse leaders in Ontario are keenly aware of the important potential that HOBIC data can provide for high-quality patient care and have identified key factors that need to be considered with the implementation of such a program.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".