The Use of a Nursing Model to Understand Diarrhea and the Role of Probiotics in Patients With Inflammatory Bowel Disease
Bibliographic record
Abstract
Inflammatory bowel disease, an umbrella term used for Crohn disease and ulcerative colitis, is often accompanied with the presenting symptom of diarrhea. This symptom can be a great nuisance and emotionally distressing to the individual with inflammatory bowel disease. Although the exact etiology of inflammatory bowel disease is still unknown, interactions between the host susceptibility, mucosal immunity, and intestinal microflora are thought to be major factors. One intervention that is gaining increasing support by the research and medical community is the use of probiotics, which work on the intestinal flora by altering the bacterial composition and thereby rendering the environment unfavorable to pathogenic organisms. The human response to illness model provides an ideal organizing framework to gain a comprehensive understanding of the human response of diarrhea in the inflammatory bowel disease population. By examining the physiological, pathophysiological, behavioral, and experiential perspectives as well as individual vulnerabilities, this model establishes sound rationale to guide nursing interventions to help the individual better cope with the physical and emotional effects of having diarrhea. This model also facilitates the provision of holistic and personalized care, which may include the use of probiotics to help alleviate this distressing symptom.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".