Developing an Innovation Culture Measurement Construct for Healthcare Organizations
Bibliographic record
Abstract
Objective: To develop an innovation culture measurement model specific to healthcare, by amending the original scale items of the Dobni innovation culture construct and model developed in 2008. Design: The project performed exploratory factor analysis from data collected on surveys, using redesigned scale items from the original Dobni innovation culture measurement. Setting: Managers and administrators from a Provincial Health Services Authority in Canada. Results: An exploratory factor analysis was performed on the 43 scale items used in the survey. The scale items were reduced to 31 and loaded on to new factors creating an Innovation Culture Measurement Model specific to healthcare. Conclusion: Although exploratory, the new model and scale items provide a foundation for researchers to advance innovation culture measurement in healthcare. Academically, measuring innovation culture has created a rich research stream, but to date has not exclusively focused on healthcare. Pragmatically, measuring innovation culture provides healthcare leaders and policy setters a benchmark to assess internally over a period of time or towards other entities.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".