Development of an educational resource for nursing staff and unlicensed personnel on the identification and prevention of urinary tract infections within residents living in long term care
Bibliographic record
Abstract
Background: Urinary tract infections (UTIs) occur within the genitourinary system and lead to serious infections resulting in hospitalization and death. Older adults are at a higher risk of developing UTIs due to decreased immunity, this may further be exacerbated by increased rates of bladder and bowel incontinence. UTIs are among the most frequently diagnosed infections in older adults and may be preventable or less severe if detected early and modifiable risk factors are addressed. Purpose: To develop an educational resource focused on the identification and prevention of UTIs for Long Term Home (LTCH) staff working with older adults and their family members. Methods included: 1) an integrative literature review 2) an environmental scan of resources related to UTIs available online within Canada and internationally 3) consultation interviews with key stakeholders and 4) the development of the educational resource. Results: Findings highlighted the need for an educational resource to support LTCH staff and families in being able to identify and prevent UTIs in older adults. The literature revealed that LTCH staff may experience knowledge deficits in identifying clinical signs and symptoms of UTI, and preventative measures. The findings from the methods informed the educational resource consisting of two infographics for LTCH staff. These infographics covered UTI risk factors for older adults, prevention interventions, special nursing considerations, the clinical signs and symptoms of infection, and diagnostic testing requirements. Conclusion: The development of the educational resource is to support LTCH staff to provide evidence-informed care to prevent and identify UTI’s within older adults living in LTCH.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".