Barriers to healthcare-worker adherence to infection prevention and control practices in British Columbia during the coronavirus disease 2019 (COVID-19) pandemic: A cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: The coronavirus disease 2019 (COVID-19) pandemic highlighted the importance of robust infection prevention and control (IPAC) practices to maintain patient and staff safety. However, healthcare workers (HCWs) face many barriers that affect their ability to follow these practices. We identified barriers affecting HCW adherence to IPAC practices during the pandemic in British Columbia, Canada. DESIGN: Cross-sectional web-based survey. SETTING: Acute care, long-term care or assisted living, outpatient, mental health, prehospital care, and home care. PARTICIPANTS: Eligible respondents included direct-care providers and IPAC professionals working in these settings in all health authorities across British Columbia. METHODS: We conducted a web-based survey from August to September 2021 to assess respondent knowledge and attitudes toward IPAC within the context of the COVID-19 pandemic. Respondents were asked to rate the extent to which various barriers affected their ability to follow IPAC practices throughout the pandemic and to make suggestions for improvement. RESULTS: The final analysis included 2,488 responses; 36% of respondents worked in acute care. Overall, perceptions of IPAC practice among non-IPAC professionals were positive. The main self-perceived barriers to adherence included inadequate staffing to cover absences (58%), limited space in staff rooms (57%), multibed rooms (51%), and confusing messages about IPAC practices (51%). Common suggestions for improvement included receiving more support from IPAC leadership and clearer communication about required IPAC practices. CONCLUSIONS: Our findings highlight frontline HCW perspectives regarding priority areas of improvement for IPAC practices. They will inform policy and guideline development to prevent transmission of COVID-19 and future emerging infections.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".