Building the foundation to generate a fundamental care standardised data set
Bibliographic record
Abstract
AIM AND OBJECTIVES: This paper provides an overview of the current state of performance measurement, key trends and a methodological approach to leverage in efforts to generate a standardised data set for fundamental care. BACKGROUND: Considerable transformation is occurring in health care globally with organisations focusing on achieving the quadruple aim of improving the experience of care, the health of populations, and the experience of providing care while reducing per capita costs of health care. In response, healthcare organisations are employing performance measurement and quality improvement methods to achieve the quadruple aim. Despite the plethora of measures available to health managers, there is no standardised data set and virtually no indicators reflecting how patients actually experience the delivery of fundamental care, such as nutrition, hydration, mobility, respect, education and psychosocial support. CONCLUSIONS: Given the linkages of fundamental care to safety and quality metrics, efforts to build the evidence base and knowledge that captures the impact of enacting fundamental care across the healthcare continuum and lifespan should include generating a routinely collected data set of relevant measures. RELEVANCE TO CLINICAL PRACTICE: This paper provides an overview of the current state of performance measurement, key trends and a methodological approach to leverage in efforts to generate a standardised data set for fundamental care. Standardised data sets enable comparability of data across clinical populations, healthcare sectors, geographic locations and time and provide data about care to support clinical, administrative and health policy decision-making.
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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 | 0.553 | 0.708 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.011 | 0.030 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".