Linking HOBIC Measures with Length of Stay and Alternate Levels of Care: Implications for Nurse Leaders in Their Efforts to Improve Patient Flow and Quality of Care
Bibliographic record
Abstract
Integral to understanding and leveraging performance data to monitor and drive quality improvement (QI) efforts to enhance patient care is a partnership between researchers (who generate data) and nurse executives (who lead QI efforts). In Canada, evidence-based, nursing-sensitive patient outcome data are included in the Health Outcomes for Better Information and Care (HOBIC) initiative. A descriptive study was undertaken to examine the relationships and predictive abilities of HOBIC measures with length of stay (LOS) and alternate levels of care (ALC) measures. Specifically, we were interested in determining (a) whether relationships among the HOBIC measures exist and (b) whether any of the HOBIC measures are associated with, and could subsequently be used to predict, the patient and the destination to which he or she is discharged (ALC). Our interest in understanding these relationships and predictive abilities was both research driven and practice driven, with the intent eventually to use study findings to target clinical practice and data feedback strategies. To address the two research aims, this study employed both descriptive and inferential statistical approaches with multiple analytic approaches. Study results suggest that many of the HOBIC measures are related, with a higher score in one measure corresponding to a higher score in another measure. The exception is the therapeutic self-care (TSC) measure, in which higher scores on other HOBIC measures were correlated with lower TSC scores. Associations were also found with the predictive ability of certain HOBIC measures on LOS and ALC. Our study findings call for nurse leaders to emphasize the importance to clinical nurses on hospital units of focusing their efforts on assisting patients in managing their fatigue and dyspnoea effectively; increasing their ability to engage in activities of daily living, functional status and therapeutic self-care; and preventing or minimizing pressure ulcers and falls in acute care patients. In turn, these efforts may decrease patients' LOS.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".