Organizational impact of evidence-informed decision making training initiatives: a case study comparison of two approaches
Bibliographic record
Abstract
BACKGROUND: The impact of efforts by healthcare organizations to enhance the use of evidence to improve organizational processes through training programs has seldom been assessed. We therefore endeavored to assess whether and how the training of mid- and senior-level healthcare managers could lead to organizational change. METHODS: We conducted a theory-driven evaluation of the organizational impact of healthcare leaders' participation in two training programs using a logic model based on Nonaka's theory of knowledge conversion. We analyzed six case studies nested within the two programs using three embedded units of analysis (individual, group and organization). Interviews were conducted during intensive one-week data collection site visits. A total of 84 people were interviewed. RESULTS: We found that the impact of training could primarily be felt in trainees' immediate work environments. The conversion of attitudes was found to be easier to achieve than the conversion of skills. Our results show that, although socialization and externalization were common in all cases, a lack of combination impeded the conversion of skills. We also identified several individual, organizational and program design factors that facilitated and/or impeded the dissemination of the attitudes and skills gained by trainees to other organizational members. CONCLUSIONS: Our theory-driven evaluation showed that factors before, during and after training can influence the extent of skills and knowledge transfer. Our evaluation went further than previous research by revealing the influence--both positive and negative--of specific organizational factors on extending the impact of training programs.
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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 | 0.065 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".