Evaluation of Organizational Culture among Different Levels of Healthcare Staff Participating in the Institute for Healthcare Improvement's 100,000 Lives Campaign
Bibliographic record
Abstract
BACKGROUND: Little is known about how hospital organizational and cultural factors associated with implementation of quality initiatives such as the Institute for Healthcare Improvement's (IHI) 100,000 Lives Campaign differ among levels of healthcare staff. DESIGN: Evaluation of a mixed qualitative and quantitative methodology ("trilogic evaluation model"). SETTING: Six hospitals that joined the campaign before June 2006. PARTICIPANTS: Three strata of staff (executive leadership, midlevel, and frontline) at each hospital. RESULTS. Surveys were completed in 2008 by 135 hospital personnel (midlevel, 43.7%; frontline, 38.5%; executive, 17.8%) who also participated in 20 focus groups. Overall, 93% of participants were aware of the IHI campaign in their hospital and perceived that 58% (standard deviation, 22.7%) of improvements in quality at their hospital were a direct result of the campaign. There were significant differences between staff levels on the organizational culture (OC) items, with executive-level staff having higher scores than midlevel and frontline staff. All 20 focus groups perceived that the campaign interventions were sustainable and that data feedback, buy-in, hardwiring (into daily activities), and leadership support were essential to sustainability. CONCLUSIONS: The trilogic model demonstrated that the 3 levels of staff had markedly different perceptions regarding the IHI campaign and OC. A framework in which frontline, midlevel, and leadership staff are simultaneously assessed may be a useful tool for future evaluations of OC and quality initiatives such as the IHI campaign.
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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 | 0.055 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".