Exploring Nurses' Perceptions of Collecting and Using HOBIC Measures to Guide Clinical Practice and Improve Care
Bibliographic record
Abstract
Ontario's Health Outcomes for Better Information and Care (HOBIC) is designed to help organizations and nurses plan and evaluate care by comparing patient outcomes with historical data on similar cases. Yet, fewer than 15% of patients in a 2010 study were found to have complete admission and discharge data sets. This low utilization rate of HOBIC measures prompted the current qualitative study, in which nurses from three clinical settings in an academic teaching hospital were interviewed to gain their perceptions related to collecting and using HOBIC measures in practice. The objective was to identify factors that promote or impede the collection and use of HOBIC data in clinical practice to improve patient care and outcomes. Analysis of interview results produced four key themes related to (a) use of HOBIC measures to inform patient care, (b) collecting and documenting HOBIC measures, (c) HOBIC as an afterthought and "black hole" and (d) impediments to assessing and documenting HOBIC measures because of language barriers, patients' cognitive status and lack of time. Recommendations to improve uptake include developing, implementing and evaluating a communication and learning plan that promotes HOBIC's values and benefits, and determining how managers and administrators perceive utilization of HOBIC at the clinical unit and organizational levels.
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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 | 0.052 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".