Preventing and managing constipation in older inpatients
Bibliographic record
Abstract
Purpose Constipation in hospitalised older adults leads to adverse events and prolonged stay. The purpose of this paper, therefore, is to effectively prevent and manage constipation in older adults undergoing inpatient rehabilitation using a multidisciplinary war on constipation (WOC) algorithm. Design/methodology/approach A quality improvement project in older adults undergoing rehabilitation for prevention and constipation management was conducted. Quality improvement "plan-do-study-act" cycles included an initial constipation audit in the wards and meetings with the multidisciplinary team (MDT) to develop an algorithm for the preventing, detecting and effectively treating constipation. Findings The project resulted in a 14 per cent reduction in constipation incidence after the newly developed WOC algorithm was introduced. The project also improved communication between patients and the MDT around patients' bowel habits. Practical implications The project shows that using quality improvement methods in rehabilitation settings, earlier detection, earlier intervention and overall reduction in constipation in older adults can be achieved. Originality/value The WOC algorithm has been developed and institutionalised in the current setting. This algorithm may also be applicable in other inpatient settings.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".