Bibliographic record
Abstract
PURPOSE: This study examined organizational and market factors associated with nursing homes that are most likely to be early adopters of innovations. Early adopter institutions, defined as the first 20% of facilities to adopt an innovation, are important because they subsequently facilitate the diffusion of innovations to others in the industry. DESIGN AND METHODS: Two groups of innovations were examined, special care units and subacute care services. I used discrete-time logistic regression analysis and nationally representative data from 13,162 facilities at risk of being early adopters of innovations during twelve 6-month intervals from 1992 to 1997. RESULTS: Organizational factors that increase the likelihood of early innovation adoption are larger bed size, chain membership, and high levels of private-pay residents. Four market factors that increase the likelihood of early innovation adoption are: a retrospective Medicaid reimbursement methodology, a more competitive environment, higher average income in the county, and a higher number of hospital beds in the county. IMPLICATIONS: This analysis shows that organizational and market characteristics of nursing homes affect their propensity toward early adoption of innovations. Some of the results may be useful for nursing home administrators and policy makers attempting to promote innovation.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".