Barriers and facilitators to the use of personal protective equipment in long-term care: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Long-term care (LTC) residents are vulnerable to invasive infection. Appropriate use and training on personal protective equipment (PPE) is important for protecting residents and healthcare workers (HCWs). Studies on the barriers and facilitators to PPE use are limited in LTC settings. AIM: To characterize HCWs' perceptions of barriers and facilitators to the uptake and appropriate use of PPE in LTC facilities in Calgary, Alberta. METHODS: Semi-structured interviews were conducted with HCWs from April to October 2022. Interview transcripts were analysed deductively to identify themes from the Theoretical Domains Framework. FINDINGS: Seven HCWs were interviewed. Barriers and facilitators fell within six overarching themes including: availability and quality of PPE; knowing how to use PPE; familial obligations; convenience and comfort; sense of professional duty; and social influences and identity. Additional factors such as understaffing and the need for more training sessions were highlighted. Strategies to improve PPE use were identified by HCWs, including the use of PPE champions, regular audits, and constructive feedback. CONCLUSION: Identification of unique barriers and facilitators regarding PPE use by HCWs in LTC will facilitate targeted interventions to improve PPE use in this setting.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".