Public awareness and knowledge of sepsis: a cross-sectional survey of adults in Canada
Bibliographic record
Abstract
BACKGROUND: Sepsis is a life-threatening complication of the body's response to infection. The financial, medical, and psychological costs of sepsis to individuals and to the healthcare system are high. Most sepsis cases originate in the community, making public awareness of sepsis essential to early diagnosis and treatment. There has been no comprehensive examination of adult's sepsis knowledge in Canada. METHODS: We administered an online structured survey to English- or French-literate adults in Canada. The questionnaire comprised 28 questions in three domains: awareness, knowledge, and information access. Sampling was stratified by age, sex, and geography and weighted to 2016 census data. We used descriptive statistics to summarize responses; demographic differences were tested using the Rao-Scott correction for weighted chi-squared tests and associations using multiple variable regression. RESULTS: Sixty-one percent of 3200 adults sampled had heard of sepsis. Awareness differed by respondent's residential region, sex, education, and ethnic group (p < 0.001, all). The odds of having heard of sepsis were higher for females, older adults, respondents with some or completed college/university education, and respondents who self-identified as Black, White, or of mixed ethnicity (p < 0.01, all). Respondent's knowledge of sepsis definitions, symptoms, risk factors, and prevention measures was generally low (53.0%, 31.5%, 16.5%, and 36.3%, respectively). Only 25% of respondents recognized vaccination as a preventive strategy. The strongest predictors of sepsis knowledge were previous exposure to sepsis, healthcare employment, female sex, and a college/university education (p < 0.001, all). Respondents most frequently reported hearing about sepsis through television (27.7%) and preferred to learn about sepsis from healthcare providers (53.1%). CONCLUSIONS: Sepsis can quickly cause life-altering physical and psychological effects and 39% of adults sampled in Canada have not heard of it. Critically, a minority (32%) knew about signs, risk factors, and strategies to lower risk. Education initiatives should focus messaging on infection prevention, employ broad media strategies, and use primary healthcare providers to disseminate evidence-based information. Future work could explore whether efforts to raise public awareness of sepsis might be bolstered or hindered by current discourse around COVID-19, particularly those centered on vaccination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".