Barriers to infection prevention and control in long-term care/assisted living settings in British Columbia during the COVID-19 pandemic: a cross-sectional survey
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic disproportionately impacted long-term care and assisted living (LTC/AL) facilities in Canada, where infection prevention and control (IPAC) programs had been suboptimal. We aimed to identify barriers affecting healthcare workers’ (HCW) adherence to IPAC practices during the pandemic in British Columbia in LTC/AL compared to acute care settings. Methods We conducted a web-based survey of direct care providers and IPAC professionals across BC from August to September 2021, focused on knowledge and attitudes toward IPAC within the context of the COVID-19 pandemic, and barriers that affected respondents’ abilities to follow IPAC practices throughout the pandemic. Results The final analysis included 896 acute care respondents and 441 from LTC/AL. More LTC/AL respondents reported experiencing the following barriers: following IPAC guidance was of lower priority compared to other tasks (29.1% vs. 14.7%, FDR = 0.001) and not their responsibility (28.0% vs. 11.2%, FDR = 0.001); limited supplies for personal protective equipment (PPE) (49.0% vs. 33.6%, FDR = 0.001), hand hygiene products (42.2% vs. 28.8%, FDR = 0.001), and cleaning/disinfection products (44.1% vs. 30.3%, FDR = 0.001); deficits in IPAC leadership support (46.2% vs. 38.9%, FDR = 0.012), IPAC education and training (46.9% vs. 32.0%, FDR = 0.001), and patient care knowledge for managing COVID-19 infections (46.6% vs. 36.0%, FDR = 0.001). Conclusions This survey found that barriers to HCWs’ adherence to IPAC practices during the COVID-19 pandemic were different in LTC/AL settings compared to acute care. Improvement efforts should focus on strengthening IPAC programs in LTC/AL, particularly enhanced IPAC staffing/leadership, increased training and education, and improving access to PPE, hand hygiene, and cleaning products.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".