Towards a National Report Card in Nursing: A Knowledge Synthesis
Bibliographic record
Abstract
this paper is an abridged version of a knowledge synthesis undertaken to inform the proceedings of a collaborative forum of nurse leaders convened under the auspices of health Canada, the academy of Canadian Executive Nurses, the Canadian Nurses association and Canada health infoway for the purpose of discussing the development of a nursing report card for Canada. the synthesis summarized the state of the science in the measurement of nursing-sensitive outcomes and the utilization of nursing report cards -information that informed forum participants' dialogue and planning. this condensed version of the synthesis focuses on initiatives related to outcomes and performance monitoring in nursing, including specific indicators and reporting systems and the development, implementation and utilization of nursing report cards. origins of outcomes/performance monitoring Efforts to identify nursing's contribution to high-quality care and to conduct research into patient outcomes date back to Nightingale However, the systematic collection of data to assess outcomes did not gain widespread attention until the late 1970s, when concerns about quality of care prompted the development of the Universal Minimum Health Data Set, which was followed shortly thereafter by the Uniform Hospital Discharge Data Set These data sets facilitated consistency in data collection among healthcare organizations by prescribing the data elements to be gathered. The aggregated data informed the assessment of care quality in hospitals and provided discharge information about 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 | 0.461 | 0.480 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.027 | 0.030 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.032 | 0.021 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".