The NOSO-COVID study: a large-scale survey assessing stakeholder perspectives on patient and family engagement in infection prevention, informed by Q-methodology findings
Bibliographic record
Abstract
BACKGROUND: Healthcare-associated infections are common in hospitalized patients, exacerbated by COVID-19. Engaging patients and families in infection prevention and control may enhance adherence, but optimal strategies remain unclear. OBJECTIVE: This study developed a large-scale survey, informed by previous Q-methodology research, to assess stakeholder perspectives on patient and family engagement in infection prevention, identify consensus and disagreement, and explore socio-demographic and work-related influences. METHODS: A cross-sectional survey was conducted in surgical and family medicine units at two Canadian university hospitals in 2023. The NOSO-COVID survey included 17 Likert-scale items derived from identified infection prevention engagement viewpoints. Data were analyzed using univariate, bivariate, and multivariable logistic regression. RESULTS: Among 212 participants (patients, family members, and staff), responses varied on infection prevention engagement strategies. Common suggestions included bedside disinfectants and monitoring staff compliance. Older participants, particularly in family medicine units, were more supportive of educational and behavioral measures. Comfort with advocating for infection prevention differed across units, with surgical unit participants expressing less confidence. Institutional culture influenced perceptions, with staff at one hospital reporting greater ease in engaging patients and families in infection prevention. Four consensus items were identified, emphasizing staff reminders for hand hygiene, availability of disinfectants, and providing accessible infection prevention information. CONCLUSIONS: Our findings suggest that tailoring IPC engagement strategies to hospital unit cultures and stakeholder needs is essential. Strategies to enhance communication, address power dynamics, and promote shared responsibilities to improve IPC should be identified and their effectiveness evaluated.
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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".