Effect of educational interventions on inpatient bowel preparation: A systematic review
Bibliographic record
Abstract
Colonoscopy is effective in screening for colorectal cancer and diagnosis of gastrointestinal disease. However, the efficacy of colonoscopy is highly dependent on the quality of bowel preparation. Inadequate bowel preparation may result in incomplete colonoscopy, increasing the patient's risk for missed adenomas, repeated procedures, increased cost, and adverse events. Educational interventions have been utilized to improve the quality of bowel preparation, however, a gap in the literature still exists on the most effective type of educational intervention. This literature review aims to examine research studies on the effect of various educational interventions in improving the quality of bowel preparation for inpatients undergoing colonoscopy. A database search was performed using the Preferred Reporting Items for Systematic Reviews and Meta-analysis methodology. The initial search of the databases and other sources identified 92 research studies. The Critical Appraisal Skills Program for qualitative studies checklist was utilized to appraise and summarize the literature selected for final review. After screening and consideration of eligibility criteria, six studies were included in the final review. The most effective educational approach to improve the quality of inpatient bowel preparation was using a smartphone application offering text, visual images, and video for instructions, followed by utilizing an educational booklet about colonoscopy. The studies that did not involve nurses during patient education showed no significant effect.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".