Recommended Common Data Elements for International Research in Long-Term Care Homes: Exploring the Workforce and Staffing Concepts of Staff Retention and Turnover
Bibliographic record
Abstract
The aim of this review is to develop a common data element for the concept of staff retention and turnover within the domain of workforce and staffing. This domain is one of four core domains identified by the WE-THRIVE ( Worldwide Elements to Harmonize Research in Long-Term Care Li ving Environments) group in an effort to establish an international, person-centered long-term care research infrastructure. A rapid review identified different measurement methods to assess either turnover or retention at facility level or intention to leave or stay at the individual staff level. The selection of a recommended measurement was guided by the WE-THRIVE group’s focus on capacity rather than deficits, the expected availability of internationally comparable data, and the goal to provide a short, ecologically viable measurement. We therefore recommend to measure staff’s intention to stay with a single item, at the individual staff level. This element, we argue, is an indicator of staff stability, which is important for reduced organizational cost and improved productivity, positive work environment, and better resident–staff relationships and quality of care.
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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 | 0.119 | 0.363 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.011 |
| Bibliometrics | 0.034 | 0.037 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.012 | 0.010 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".