Factors influencing self-reported facial-protective equipment adherence among home care nurses and personal support workers: A multisite cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Facial-protective equipment (FPE) use increased during the SARS-CoV-2 pandemic. This study explored factors influencing home care personal support workers' (PSWs) and nurses' self-reported adherence to FPE. METHODS: A cross-sectional, electronic, survey was distributed to PSWs and nurses (1,108 complete responses) at 3 home care agencies in Ontario, Canada, in May to June 2022. Descriptive, bivariate, and multivariable analysis were used to assess individual, environmental, and organizational factors influencing adherence. RESULTS: Among participants (786 PSWs, 322 nurses), 64% reported being adherent to both respiratory and eye FPE (Respiratory: 96%, Eye: 64%). Higher adherence was associated with facility-based work; better knowledge of FPE; prepandemic FPE use; good availability and convenient access; strong organizational support for health and safety; and Caribbean identity. Lower adherence was found for men; nurses with 2-year diplomas; shorter length of employment; communal transportation; and experiencing negative mental health effects from workplace infectious disease exposure. DISCUSSION: Agencies should prioritize increasing providers' knowledge of FPE, supporting mental well-being, fostering a supportive culture, and ensuring availability of FPE. The influences of gender, ethnicity, and role require further exploration. CONCLUSIONS: FPE adherence may be improved by addressing modifiable factors and developing population-specific strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".