A qualitative examination of changing practice in Canadian neonatal intensive care units
Bibliographic record
Abstract
OBJECTIVE: The goal was to explore the perspectives of health care professionals on factors that influence change to policies, protocols and practices in the Neonatal Intensive Care Unit (NICU) with regard to nosocomial infection and chronic lung disease. Study design An exploratory descriptive design using semi-structured individual and focus group interviews was used. Individual interviews (n=76) and focus group sessions (n=14 with a total of 78 participants) were conducted for a total of 154 health professional participants. METHODS: Mayring's qualitative content analysis approach was used to analyse the data. All interviews were audio-taped, transcribed and analysed using inductive reasoning. The data were then organized into categories that reflected emerging themes. RESULTS: Seven categories that influenced practice change were derived from the data including staffing issues, consistency in practice, the approval process, a multidisciplinary approach to care, frequency and consistency of communication, rationale for change and the feedback process. These categories were further delineated into three emerging themes related to human resources, organizational structure and communications. Pettigrew's conceptual framework provided a lens to view the results in relation to the process of change. CONCLUSIONS: This study has helped to further our understanding of individual and organizational factors that facilitate and hinder changes in clinical practice in the NICU. These factors will be used as a starting point for organizational change to enhance infant outcomes in the NICU.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| 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".