Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it