Individual emergency preparedness survey among Canadians in Lower Mainland, BC
Bibliographic record
Abstract
Background: BC residents are prone to natural disasters and emergencies such as earthquakes and prolonged power outage due to severe weather and flooding. To minimize and mitigate the impacts, individuals should prepare in advance for any potential emergencies. There are studies showing only half of the Canadians, in general, are well prepared. Concrete evidence of factors affecting individual’s emergency preparedness are not clear. Therefore, this research study aims to investigate the association between BC residents’ emergency preparedness level and demographic/socio-economic factors. Methods: Housed on SurveyMonkey, the online self-administered survey was distributed via Facebook and Reddit to survey local BC residents. The survey was posted on sub-groups based on topic-relevance and geographic areas that are located within Lower Mainland. The sampling period is approximately one month, which the results were analyzed by the NCSS program. Results: Overall, less than half (41%) of the participants reported to have an emergency kit at home. The chi-square test results show that two factors, language (p=0.025) and status of occupancy (p=0.048) are significantly associated with level of emergency preparedness. Conclusion: There are significant associations between level of emergency preparedness and demographic factors - language barrier and status of occupancy. People who do not use English as their primary language found to be less prepared to those who use English as their primary language. Renters, as compared to homeowners, found to be less prepared as well. This serves as supporting data and evidence to transit these findings to promote emergency readiness among residents in Metro Vancouver.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".