The Preventive Use of Probiotics Against Gastrointestinal HAIs in Ontario
Bibliographic record
Abstract
Given confounding evidence in the literature and the lack of standardized guidelines in Canada, little is known about the process through which clinical dieticians decide whether or not to implement a probiotics course with the intent of preventing gastrointestinal healthcare-associated infections (HAIs) in Ontario. This study uses a mixed-method design featuring a content analysis of the literature and a survey addressed to clinical dietitians in Ontario to investigate (a) the likelihood of clinical dietitians in recommending probiotics course to prevent gastrointestinal healthcare-associated infections (HAIs) and (b) their reasoning in setting certain parameters of such a probiotics course. Among the four respondents obtained for the survey, all ranked their likelihood of prescribing probiotics to prevent nosocomial gastrointestinal infections as medium or less. The determination of parameters in a course were largely influenced by guidelines and/or evidence, case-specific consideration and fixed procedures, as these were mentioned among other themes by the largest number of respondents and were included in the largest amount of questions on average. Finally, parameters which the majority of respondents determined using the same themes were the dose of probiotics, timing of the course with regards to antibiotics, and type of probiotics. Keywords: Probiotics, Gastrointestinal infections, Nosocomial infections, HAIs, Gut flora, Antibiotics, Antimicrobial resistance
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".