Advancing Long-Term Care Science Through Using Common Data Elements: Candidate Measures for Care Outcomes of Personhood, Well-Being, and Quality of Life
Bibliographic record
Abstract
To support the development of internationally comparable common data elements (CDEs) that can be used to measure essential aspects of long-term care (LTC) across low-, middle-, and high-income countries, a group of researchers in medicine, nursing, behavioral, and social sciences from 21 different countries have joined forces and launched the Worldwide Elements to Harmonize Research in LTC Living Environments (WE-THRIVE) initiative. This initiative aims to develop a common data infrastructure for international use across the domains of organizational context, workforce and staffing, person-centered care, and care outcomes, as these are critical to LTC quality, experiences, and outcomes. This article reports measurement recommendations for the care outcomes domain, focusing on previously prioritized care outcomes concepts of well-being, quality of life (QoL), and personhood for residents in LTC. Through literature review and expert ranking, we recommend nine measures of well-being, QoL, and personhood, as a basis for developing CDEs for long-term care outcomes across countries. Data in LTC have often included deficit-oriented measures; while important, reductions do not necessarily mean that residents are concurrently experiencing well-being. Enhancing measurement efforts with the inclusion of these positive LTC outcomes across countries would facilitate international LTC research and align with global shifts toward healthy aging and person-centered LTC models.
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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 | 0.002 | 0.001 |
| 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".