Bibliographic record
Abstract
Background and aims: Descriptions of effective implementation strategies for system-wide innovation in health care are incomplete. Aims: We evaluated the implementations of a complex inter-professional intervention in acute hospital settings to describe differences in approach and the relative impact on innovation adoption. Methods: A prospective observational study of 4 international hospitals implementing the BedsidePEWS innovation was performed. Two implementation team members completed a qualitative review of data obtained from audiotapes and notes of weekly meetings with each hospital team. Thematic analysis identified common domains in implementation approaches. These domains were validated with the site teams and grouped into the PHARIS framework (evidence, facilitation, and context) for further analysis. Results: Data was synthesized from 109 hour-long meetings (22-34 per team). Three implementation domains were identified along with seven domain –specific items; socially embeded process (front line engagement, use of champions), organizational level influences (audit and feedback, motivation for organizational change, implementation team characteristics) and fit of the innovation (customization, education). In the domains of social process and innovation fit we found that implementation items manifested across a continuum of influence from facilitator to barrier. Organization influences for change were primarily internally driven across all sites. The composition of implementation teams differed between sites as being either research or clinically situated. Conclusions: Future quantification of these items for evaluation of relationships and the relative importance of each to innovation adoption is planned.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.336 | 0.125 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".