Assessing Hand Hygiene and Low-Level Disinfection of Equipment Compliance in an Acute Care Setting: Mixed Methods Approach
Bibliographic record
Abstract
Background Hand hygiene and low-level disinfection of equipment behaviors among hospital staff are some of the leading cost-effective methods to reduce hospital-acquired infections (HAI) among patients. Objective The aim of this study is to examine hand hygiene and low-level disinfection of equipment practices in a central Texas hospital and to explore pertaining gaps, perceptions, and challenges. Methods Data were collected using a multipronged mixed methods approach that included the following: (1) observation of hand hygiene and low-level disinfection practices (12 and 8 units during morning and evening shifts, respectively); (2) observation of usability/placement of hand sanitizer dispensers; (3) semistructured interviews; and (4) a follow-up email survey. Results In total, 222 (156 morning shift and 66 evening shift) staff members were observed. Of 526 hand hygiene and 33 low-level disinfection opportunities, compliance was observed 410 (78%) and 17 (51%) times, respectively. Overall, 6 units (50%) had ≥0.80 (favorable) hand hygiene compliance during the morning shift and 2 units (25%) had ≥0.80 hand hygiene compliance during the evening shift. Aggregated low-level disinfection compliance was 0.54 during the morning and 0.33 during the evening. Overall, the odds of noncompliant hand hygiene behavior were 1.4 times higher among staff who worked during night shifts compared to day shifts; however, this relationship was not statistically significant (95% CI 0.86-2.18; P=.18). Noncompliant behavior was most likely among unit B staff during the evening; however, this relationship was not statistically significant (OR 5.3, 95% CI 0.84-32.9; P=.07) All units, except one, had similar hand sanitizer dispenser usability characteristics. In the qualitative part of the study, the following challenges were identified: “shortage of time while seeing patients,” “sometimes the staff forgets,” “concern about drying hands,” “behavior is difficult or requires reminders,” and “there may be issues with resources or access to supplies to perform these behaviors.” Staff also stated that “a process that is considered effective is the Stop the Line program,” and that the “behavior is easy and automatic.” Conclusions Hand hygiene and low-level disinfection compliance is dependent on several personal and nonpersonal factors. Issues such as time constraints, peer pressure, work culture, available resources, and understanding of guidelines could influence staff behavior. The information collected through this study can be used to re-examine similar or related issues at a larger scale.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".