Patient and Family Engagement in Infection Prevention During the COVID-19 Pandemic: A Q-Methodology Study with Stakeholders from a Canadian University Health Care Center
Bibliographic record
Abstract
BACKGROUND: Health care-associated infections are frequent complications for hospitalized patients, and the COVID-19 pandemic exacerbated this issue. This study aimed to explore stakeholders' viewpoints on how patients and families should engage in preventing health care-associated infections in hospital settings. METHODS: The authors employed Q-methodology, a mixed methods approach combining by-person factor analysis with in-depth interviews to capture shared viewpoints among participants. The research was conducted in a university-affiliated adult transplant unit using a purposive sample of staff members, patients, and family members. Participants ranked 40 preselected statements on a tablet using the Q-sorTouch Web application (for example, "Staff members should check that patients and family members wash their hands at key moments") on a continuum from "most agree" (+2) to "most disagree" (-2). Participants then took part in in-depth interviews to elaborate on their rankings. Data analysis included factor extraction and thematic interpretation. RESULTS: Nineteen participants completed the study. Analysis revealed three distinct viewpoints on patient and family engagement in infection prevention and control: (1) a controlling approach in which health care professionals ensure patient and family compliance, (2) an enabling approach that supports shared responsibility and emphasizes autonomy, and (3) a view of patients and family members as vigilant partners. Seven consensus statements emerged, indicating agreement on strategies in which patients and families are passive rather than proactive. CONCLUSION: Although a paternalistic model emphasizing health care professional oversight prevailed, alternative perspectives emerged advocating for greater autonomy and responsibility among patients and families. These differing opinions indicate ongoing debate about how best to involve patients and their families in infection control, particularly during periods of heightened risk.
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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 | 0.041 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.025 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".