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Record W1848997551 · doi:10.1542/peds.111.se1.e419

Using Organizational Assessment Surveys for Improvement in Neonatal Intensive Care

2003· article· en· W1848997551 on OpenAlexaff
G. Ross Baker, Hannah King, Jeanne L. MacDonald, Jeffrey D. Horbar

Bibliographic record

VenuePEDIATRICS · 2003
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNeonatal intensive care unitMedicineTeamworkOrganizational cultureQuality managementUnit (ring theory)Survey data collectionNursingIntensive careIntensive care unitMedical educationPublic relationsPsychologyOperations managementManagementManagement systemPediatrics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.554
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.095
GPT teacher head0.442
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations42
Published2003
Admission routes1
Has abstractyes

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