Risk communication with nurses during infectious disease outbreaks: Learning from SARS
Bibliographic record
Abstract
Objective: To identify gaps in risk communication during public health emergencies as identified by nurses who worked in critical and emergency care hospital units during the Severe Acute Respiratory Syndrome (SARS) outbreak in Canada.Design: This research is part of a larger multimethod study of the psychosocial impacts of the SARS outbreak in Canada for healthcare workers. For this qualitative analysis of risk communication, focus groups were conducted in four Canadian cities using purposive sampling to study perspectives of frontline critical care and emergency department nurses. Covello’s (2003) model of best practices in risk communication is applied to assess specific areas in which risk communication gaps were identified by nurses interviewed in the focus groups.Setting: Five focus groups held in four Canadian cities: Halifax, Ottawa, Toronto, Vancouver.Participant/Data: n _ 100 participated in focus groups in four urban communities.Results: During the SARS outbreak in 2003, high levels of uncertainty, lack of trust, and questions about leadership credibility emerged as important risk communication challenges. Communication problems were compounded by a lack of reliable information, frequent changes in infection control guidelines and risk avoidance messages, as well as contradictory actions of management and senior leaders.Conclusions: Risk communication constitutes an important component of any emergency protocol. This study of nurses working in emergency and critical care hospital settings during the 2003 SARS outbreak indicates key areas in which risk communication could be more efficient to address nurses’ concerns related to managing uncertainty, occupational health and safety, and employee quality of life. Recommendations useful for planning of any pandemics including H1N1 are derived.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".