Exploring a Common Data Element for International Research in Long-Term Care Homes: A Measure for Evaluating Nursing Supervisor Effectiveness
Bibliographic record
Abstract
The aim of this study is to recommend a common data element (CDE) to measure supervisory effectiveness of staff working in LTC homes that can be used in international research. Supervisory effectiveness can serve as a CDE in an effort to establish an international, person-centered LTC research infrastructure in accordance with the aims of the WE-THRIVE group (Worldwide Elements to Harmonize Research in Long Term Care Living Environments). A literature review was completed and then a panel of experts independently reviewed and prioritized appropriateness of the measures with mindfulness of their potential applications to international LTC settings. The selection of a recommended CDE measure 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, specifically for low- and middle-income countries. Two measures were considered as the CDE for supervisory effectiveness, Benjamin Rose Relationship Scale and the Supervisory Support Scale; however, given that the latter measure has been translated in Spanish and Chinese and has been tested with nursing assistants in both of these countries with good psychometric properties, our group recommends it as the CDE going forward.
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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 | 0.099 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".