A New Benchmarking Method to Advance the Two-Model DEA Approach: Evidence from a Nursing Home Application
Bibliographic record
Abstract
Shimshak and Lenard (2007) [Shimshak, D. G. and Lenard, M. L. (2007), “A two-model approach to measuring operating and quality efficiency with DEA”, INFOR, 45 (3): 143–151] introduced the Two-Model DEA (TM-DEA) approach for selecting high-operating and high-quality benchmarks in a nursing home case, in which the DEA outputs were derived from operating and quality performance objectives. This work proposes a Two-Objective DEA (TODEA) method, which enhances TM-DEA via three major features: (1) solution procedures do not require value judgments; (2) no DMUs (decision making units) are excluded from analysis; and (3) identified benchmark DMUs are not dominated by the corresponding inefficient DMUs under either operating or quality objective. To clarify the benefits of the proposed method, TODEA was compared with TM-DEA and classical DEA techniques in the nursing home example.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".