Factors influencing nursing professionals’ adherence to facial protective equipment usage: A comprehensive review
Bibliographic record
Abstract
BACKGROUND: Facial protective equipment (FPE) adherence is necessary for the health and safety of nursing professionals. This review was conducted to synthesize predisposing, enabling, and reinforcing factors that influence FPE adherence, and thus inform efforts to promote adherence. METHODS: Articles were collected using Cumulated Index to Nursing and Allied Health Literature and MEDLINE and screened for inclusion. Included articles were original studies focused on FPE adherence by nurses to prevent respiratory infection which contained occupation-specific data from at least 10 individuals and were published in English between January 2005 and February 2022. RESULTS: Thirty articles were included, 21 of which reported adherence rates. Adherence ranged from 33% to 100% for respiratory protection and 22% to 100% for eye protection. Predisposing demographic factors influencing adherence included tenure and occupation, while modifiable predisposing factors included knowledge and perception of FPE, infection transmission, and risk. Enabling factors included geography, care settings, and FPE availability. Reinforcing factors included organizational support for health and safety, clear policies, and training. CONCLUSIONS: The identified demographic factors suggest populations that may benefit from targeted interventions, while modifiable factors suggest opportunities to enhance education as well as operational processes and supports. Interventions that target these areas have the potential to promote adherence and thereby improve the occupational safety of nurses.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".