Laying Down the Groundwork for an International Measurement Infrastructure
Bibliographic record
Abstract
Abstract The COVID-19 epidemic has brought to light the significant problems in the long-term care (LTC) sector, specifically the lack of an infrastructure to collect and aggregate data between LTC sectors in different countries. This talk will briefly describe goals of the WE-THRIVE initiative, and focus on exploring the development of “workforce and staffing” common data elements for LTC. We will describe how the subgroup is “laying down the groundwork” within this domain with various methodologies to develop CDEs related to workforce and staffing. The CDEs aim to measure staff retention and turnover, evaluating nursing supervisor effectiveness, and staff training in LTC. Anticipated challenges of this international work will also be highlighted. International research on LTC can valuably inform LTC policy and practice, and the proposed CDEs can facilitate data sharing and aggregation internationally, including low-, middle-, and high-income countries.
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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 | 0.363 | 0.491 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.025 | 0.050 |
| Open science | 0.010 | 0.036 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".