Voices of Care: A mixed-methods study of key factors for enhancing support workers’ engagement in infection prevention and control practices in Canadian long-term care homes
Bibliographic record
Abstract
Background: Effective infection prevention and control (IPAC) practices are critical for preventing transmission of disease and maintaining population health, especially in long-term care homes (LTCHs). Canadian LTCHs rely extensively on personal support workers (PSWs) for care and IPAC implementation. However, their experiences are under-represented in IPAC programming within the LTCH settings. This study uses mixed methods to describe the perspectives of PSWs and identify factors influencing their IPAC knowledge to inform inclusive and effective IPAC implementation strategies in LTCHs. Methods: We surveyed a convenience sample of PSWs working in Canadian LTCHs (N = 1,166). Participants responded to a series of questions on IPAC knowledge, responsibilities, drivers, and barriers to compliance. Results: The findings indicated strong IPAC knowledge, and the willingness to learn more about advanced topics like antimicrobials and medication side effects. Being an agency staff (third party contract employee) was associated with lower IPAC knowledge while feeling comfortable asking IPAC questions and feeling respected by colleagues were associated with higher IPAC knowledge. A shortage of personal protective equipment was not identified as a barrier to IPAC practices. Respondents identified that quality training, professional development opportunities, and supportive work environments would improve PSW engagement in IPAC. Conclusion: This study highlights the impact of interprofessional relationships on PSW’s IPAC perceptions, knowledge, and engagement. PSWs represent the most abundant resources in LTCHs and by bringing their experiences to light, their expertise at the frontlines can be leveraged to improve care in Canadian LTCHs.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".