Antecedents of Clinical Information Technology Sophistication in Hospitals
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.
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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 | 0.004 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".