Antecedents of Clinical Information Technology Sophistication in Hospitals
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
Grounded in the resource-based theory and the innovation diffusion theory, this article develops and tests a research model for assessing the antecedents of hospital innovativeness with regard to clinical information technology (IT) applications. A cross-sectional survey was conducted in a sample of U.S. hospitals (n = 74) to assess three dimensions of clinical IT sophistication. Secondary data were used to measure the antecedents, namely, four groups of organizational capacity variables. Bivariate and regression analyses were conducted to identify significant associations. A significant percentage (45-61%) of the variance in clinical IT sophistication was explained, mostly by leadership and knowledge sharing capacities. In particular, IT tenure and technical knowledge resources were significantly related to clinical IT sophistication. Surprisingly, managerial tenure and hospital's belonging to a network showed significant negative associations with two dimensions of the clinical IT sophistication construct. To address the challenges they face, hospitals should consider encouraging career development for current individuals in charge of IT activities, and attracting professionals with an IT background who have the knowledge and ability to trigger new ideas and favor the adoption and use of clinical IT applications in these settings.
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 | 0.001 |
| 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