The Ontario Nursing Workload Demonstration Projects: Rethinking How We Measure, Cost and Plan the Work of Nurses
Bibliographic record
Abstract
BACKGROUND: In 2008 the Nursing Secretariat of Ontario's Ministry of Health and Long-Term Care formed a Nursing Workload Steering Committee to oversee the implementation of three demonstration projects with the objectives: to assess the feasibility of Health Outcomes for Better Information and Care (HOBIC) data as a measure of nursing workload, determine the indicators that best support nurse leaders to measure nursing work and make informed staffing decisions, and develop a model that predicts acute care nursing costs. RESULTS: Three HOBIC scales--activities of daily living (ADLs), continence and fatigue--explained a small amount of the variance in nurse judgment of the amount of nursing time patients require in the first 24 hours of care. Nurses in the study appreciated providing their professional judgment to help estimate the nursing work requirements of patients. The priority and secondary indicators most important for decision-making included medical severity of patients, environmental complexity, nurse experience, patient turnover, nurse-to-patient ratio, cognitive status, infection control, nurse vacancy, predictability of patient types, nursing interventions, patient volumes, co-morbidities, patient self-care abilities, physical and psychosocial functioning, unit type and medical diagnosis. A fairly robust model was developed using existing data sources to estimate nursing input into a patient's costs. The model explained between 69% and 80% of the variation in nursing costs for each patient. CONCLUSION: In order to effectively measure, plan and cost nursing, we need to determine what nursing is. In the future, recognition of nurses as knowledge workers will require us to consider the many patient and environmental factors that affect the ability of nurses to apply their professional judgment to care for 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 | 0.029 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".