Oral care knowledge, attitude and practice among nursing staff in acute hospital settings in Hong Kong
Bibliographic record
Abstract
Investigating the oral care delivered by nursing staff in acute hospital setting is having a remarkable shortage within the current literature. This was provoked due to lack of previous performed investigation in the acute hospital setting besides inconsistent existence of a standardized and comprehensive oral care knowledge, attitude and practice (KAP) instrumentation. Therefore, the purpose of this study is to assess the oral care KAP level for inpatients among nursing staff; to identify possible barriers to the provision of oral care; and to identify training preferences to improve the oral care of inpatients, in acute hospital settings in Hong Kong; and to provide standardized comprehensive KAP based assessment tool that would benefit and guide other future studies. In this study, a cross-sectional survey was conducted after a 55-item self-administered structured questionnaire was developed. A modified KAP tool was developed. The tool includes 4 domains: oral care knowledge, attitude, practice, and experience. Nursing staff was recruited from July 2018 to April 2019 via convenience sampling. Either online or printed questionnaires were completed. Proportions of nursing staff with good KAP, as defined by having 60% of the total score in the respective domain, were estimated with 95% confidence intervals (CI). Analysis of covariance was used to compare the mean scores of KAP among different independent variables and identify the factors associated with good KAP. 404 nursing staff were recruited. Approximately 29.5%, 33.7% and 14.9% of the respondents had good oral care knowledge, attitude and practice, respectively, and 53.2% of the respondents had unpleasant oral care experience. Better oral care practice was associated with higher levels of oral care knowledge (β = 0.1) and oral care attitude (β = 0.3). To conclude: nursing staff in acute hospital settings reported low levels of oral care KAP with variations between the RN, EN and HCA. This study adds to the literature the association between oral care unpleasant experiences and the oral care practice, as well as oral care knowledge and attitude which also in turns associated with practice. The developed standardised tool could be applied for future studies. Recommendations on the future research, training and practices were made.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".