Quality Nutrition Care: Measuring Hospital Staff’s Knowledge, Attitudes, and Practices
Bibliographic record
Abstract
Understanding the knowledge, attitudes, and practices (KAP) of hospital staff is needed to improve care activities that support the detection/prevention/treatment of malnutrition, yet quality measures are lacking. The purpose was to develop (study 1) and assess the administration and discriminative potential (study 2) of using such a KAP measure in acute care. In study 1, a 27-question KAP questionnaire was developed, face validated (n = 5), and tested for reliability (n = 35). Kappa and Intraclass Correlation (ICC) were determined. In study 2, the questionnaire was sent to staff at five diverse hospitals (n = 189). Administration challenges were noted and analyses completed to determine differences across sites, professions, and years of practice. Study 1 results demonstrate that the knowledge/attitude (KA) and the practice (P) subscales are reliable (KA: ICC = 0.69 95% CI 0.45–0.84, F = 5.54, p < 0.0001; P: ICC = 0.84 95% CI 0.68−0.92, F = 11.12, p < 0.0001). Completion rate of individual questions in study 2 was high and suggestions to improve administration were identified. The KAP mean score was 93.6/128 (range 51–124) with higher scores indicating more knowledge, better attitudes and positive practices. Profession and years of practice were associated with KAP scores. The KAP questionnaire is a valid and reliable measure that can be used in needs assessments to inform improvements to nutrition care in hospital.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".