Strengthening International Research in Long-Term Care: Recommended Common Data Elements to Support Clinical Staff Training
Bibliographic record
Abstract
The purpose of this study is to develop candidate common data element (CDE) items related to clinical staff training in long-term care (LTC) homes that can be used to enable international comparative research. This paper is part of the WE-THRIVE ( Worldwide Elements to Harmonize Research in Long-Term Care Li ving Environments) group’s initiative which aims to improve international academic collaboration. We followed best practices to develop CDEs by conducting a literature review of clinical staff (i.e., Regulated Nurses, Health Care Aides) training measures, and convening a subgroup of WE-THRIVE experts to review the literature review results to develop suitable CDEs. The international expert panel discussed and critically reflected on the current knowledge gaps from the literature review results. The panel proposed three candidate CDEs which focused on the presence of and the measurement of training. These three proposed CDEs seek to facilitate international research as well as assist in policy and decision-making regarding LTC homes worldwide. This study is a critical first step to develop candidate CDE items to measure staff training internationally. Further work is required to get feedback from other researchers about the proposed CDEs, and assess the feasibility of these CDEs in high and low resourced settings.
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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 | 0.506 | 0.679 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.022 | 0.025 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".