Revised Questionnaire to Assess Barriers to Adequate Nutrition in the Critically Ill
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to revise and improve a questionnaire to assess barriers to providing adequate enteral nutrition (EN) in critically ill adults. METHODS: Changes were made to the questionnaire based on feedback from previous respondents. The revised questionnaire, including 20 potential barriers, was pilot tested in 3 hospitals in North America. Nurses were asked to rate each item based on the degree to which it hinders the provision of EN in their intensive care unit (ICU). The acceptability of the revised questionnaire was evaluated using 5 open-ended questions appended at the end of the questionnaire. RESULTS: A total of 81 nurses completed the revised barriers questionnaire. A total of 72 of 73 (99%) respondents felt that the questionnaire was easy to understand, and 64 of 73 (88%) felt that the individual questions were clear. On average, respondents rated the degree to which potential barriers hindered the delivery of EN to the patient as "very little" or "a little." Statistically significantly differences in mean responses were observed across the 3 ICUs for 8 of the 20 items. The indices of internal reliability were assessed to be acceptable. CONCLUSIONS: The revised questionnaire to assess barriers to EN seems acceptable and clinically sensible and now appears to comprehensively list all possible modifiable barriers to delivering EN. This questionnaire needs further study to determine whether measuring barriers with this questionnaire can translate into improved EN delivery to critically ill patients.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".