Impact of interprofessional education on noninvasive ventilation in a tertiary neonatal intensive care unit.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact and effectiveness of an experiential interprofessional education workshop on noninvasive ventilation (NIV) in the setting of a neonatal intensive care unit. METHODS: In the present cross-sectional study, a full-day workshop, consisting of didactic and hands-on components, was developed to assess knowledge and perceptions, and to disseminate the latest evidence and practical aspects of NIV use. All health care professionals (HCPs) were asked to participate. Pre- and post-participation questionnaires and knowledge tests were used to assess the effectiveness of knowledge transfer, and to seek participants' reflections on the utility of the workshop. RESULTS: Among 214 participants, 206 (96%) and 195 (91%) completed the pre- and post-participation questionnaires, respectively. The majority agreed (14%) or strongly agreed (75%) that NIV education was important for their role. Participants scored their perceived comfort with NIV following the workshop highly (median 5 [interquartile range (IQR) 1]) on a five-point Likert scale and 96% would recommend it to a colleague. Median knowledge scores on NIV, assessed as percent correct responses, increased from 74% (IQR 16) to 86% (IQR 11) (P<0.05). CONCLUSIONS: A focused, context-specific workshop helped improve understanding and comfort among HCPs while reducing misconceptions about NIV. Further research to assess optimal delivery of NIV education and impact on patient outcomes is required.
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 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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".