Ontario: Linking Nursing Outcomes, Workload and Staffing Decisions in the Workplace: The Dashboard Project
Bibliographic record
Abstract
Research shows that nurses want to provide more input into assessing patient acuity, changes in patient needs and staffing requirements. The Dashboard Project involved the further development and application of an electronic monitoring tool that offers a single source of nursing, patient and organizational information. It is designed to help inform nurse staffing decisions within a hospital setting. The Dashboard access link was installed in computers in eight nursing units within the Hamilton Health Sciences (HHS) network. The Dashboard indicators are populated from existing information/patient databases within the Decision Support Department at HHS. Committees composed of the unit manager, staff nurses, project coordinator, financial controller and an information controller met regularly to review the Dashboard indicators. Participants discussed the ability of the indicators to reflect their patients' needs and the feasibility of using the indicators to inform their clinical staffing plans. Project findings suggest that the Dashboard is a work in progress. Many of the indicators that had originally been incorporated were refined and will continue to be revised based on suggestions from project participants and further testing across HHS. Participants suggested the need for additional data, such as the time that nurses are off the unit (for code blue response, patient transfers and accompanying patients for tests); internal transfers/bed moves to accommodate patient-specific issues and particularly to address infection control issues; deaths and specific unit-centred data in addition to the generic indicators. The collaborative nature of the project enabled staff nurses and management to work together on a matter of high importance to both, providing valuable recommendations for shared nursing and interprofessional planning, further Dashboard development and project management.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".