Knowledge, attitudes, and practice about protective ventilation among physical therapists
Bibliographic record
Abstract
OBJECTIVES: To measure knowledge, attitudes and practice (KAP) towards protective ventilation and identify factors associated with KAP among physical therapists in a country-wide survey. METHODS: We conducted an online survey using a validated questionnaire with 55 items including individual and institutional information, KAP, and barriers to protective ventilation. The survey was distributed by email and social media. We calculated a total KAP score and scores for knowledge, attitudes, and practice, using a standardized scale from 0 to 100. RESULTS: We included 408 participants from all states of Brazil. Median knowledge score was 80 (IQR 72-88) out of 100, with 95% respondents agreeing that they were familiar with the ventilatory settings to achieve protective ventilation, but 34% reported that airway pressures are not always discussed during rounds. Total KAP score had a median of 71 (62-79) out of 100. In the multivariate analysis, years of ICU experience, attending conferences, and ICU beds per physical therapist were independently associated with KAP score. The most significant barriers to protective ventilation were lack of education to provide low tidal volume ventilation and maintaining protective ventilation in pressure support. Participants reported there was an increase in the practice of protective ventilation during COVID-19 pandemic. CONCLUSIONS: In this countrywide study, physical therapists had good knowledge, attitudes, and practice regarding protective ventilation, and lack of education was an important factor associated with KAP. Discussing airway pressures during ICU rounds and developing specific training may improve awareness and practice of protective ventilation and impact patient outcomes.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".