Adoption of infection prevention and control practices by healthcare workers in Québec: A qualitative study
Bibliographic record
Abstract
OBJECTIVE: To describe barriers and facilitators to the adoption of recommended infection prevention and control (IPC) practices among healthcare workers (HCWs). METHODS: A qualitative research design was used. Individual semistructured interviews with HCWs and observations of clinical practices were conducted from February to May 2018 in 8 care units of 2 large tertiary-care hospitals in Montreal (Québec, Canada). RESULTS: We interviewed 13 managers, 4 nurses, 2 physicians, 3 housekeepers, and 2 medical laboratory technologists. We conducted 7 observations by following IPC nurses (n = 3), nurses (n = 2), or patient attendants (n = 2) in their work routines. Barriers to IPC adoption were related to the context of care, workplace environment issues, and communication issues. The main facilitator of the IPC adoption by HCWs was the "development of an IPC culture or safety culture." The "IPC culture" relied upon leadership support by managers committed to IPC, shared belief in the importance of IPC measures to limit healthcare-associated infections (HAIs), collaboration and good communication among staff, as well as proactivity and ownership of IPC measures (ie, development of local solutions to reduce HAIs and "working together" toward common goals). CONCLUSIONS: Adoption of recommended IPC measures by HCWs is strongly influenced by the "IPC culture." The IPC culture was not uniform within hospital and differences in IPC culture were identified between care units.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".