Using Organizational Assessment Surveys for Improvement in Neonatal Intensive Care
Bibliographic record
Abstract
BACKGROUND: Problems with organizational culture, lack of or poor team communications, and conflict are often seen as barriers to improvement efforts. METHODS: A survey measuring aspects of organizational culture was administered twice to staff in neonatal intensive care units participating in the Neonatal Intensive Care Unit Quality Improvement Collaborative Year 2000 collaborative. The surveys provided comparative data on coordination, teamwork and leadership, conflict management, unit leadership and unit culture. These data were summarized and fed back to NICU teams with guidance on their use. Interviews on the use of the survey were held with 12 medical directors and patient care leaders in 9 different NICUs. RESULTS: The findings indicated that all the units contacted saw themselves as committed to undertaking the organizational survey and using the results. Some units shared the data widely and initiated changes. Other units limited the distribution of data to the unit leadership. There was no apparent relationship between scores on the survey and activities undertaken. Several respondents credited the survey with helping to promote discussions about organizational and team issues. CONCLUSIONS: Future use of the survey should include additional materials to assist in disseminating the results to staff.
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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 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".