Factors influencing inpatient bowel preparation: a scoping review
Bibliographic record
Abstract
BACKGROUND AND AIM: Inpatients undergoing colonoscopy experience a higher-than-average rate of inadequate bowel preparation (compared to outpatients) leading to canceled procedures, increased stress on the patient, increased time in hospital, and increased cost to the healthcare system. The aim of this scoping review was to identify research surrounding inpatient bowel preparation and to identify modifiable and non-modifiable factors that influence the adequacy of bowel preparation in hospitalized patients undergoing colonoscopy and establish areas where nursing interventions may help improve overall bowel preparation rates. METHODS: An initial search of MEDLINE, CINAHL, Scopus, and Embase was undertaken to identify seed articles, followed by a structured search using keywords and subject headings. Studies conducted between 2000 and 2022 and published in English were included. A total of 37 full-text studies were screened for inclusion, with 22 meeting inclusion criteria. RESULTS: Advanced age, decreased mobility, constipation, extended length of stay, and multiple comorbidities were identified as non-modifiable factors associated with inadequate bowel preparation. Narcotic use, failure to follow preparation instruction, and delayed time to colonoscopy were identified as modifiable factors associated with poor bowel preparation. CONCLUSIONS: Educational interventions and interprofessional programs, using a multifaceted approach, increase the odds of adequate bowel preparation, including nursing tip sheets, troubleshooting flowsheets, and bowel movement assessment scoring.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".