Factors Affecting Household Disaster Preparedness: A Study of the Canadian Context
Bibliographic record
Abstract
This study addresses the issue of household disaster preparedness. This work contributes two elements to disaster research. The first contribution improve the knowledge of the factors that affect household disaster preparedness. The review of literature yielded three categories of variables that can jointly explain household disaster preparedness: household structure, demographics, and risk-perception factors. In this study 19 variables compose these factors. A second contribution constitutes a theoretical exploration of the concept of disaster preparedness. In this work, four different constructs of disaster preparedness were tested. These constructs include material preparedness, preparedness activities, a combined index, and a weighted and combined index. The study presents the logic and methodology of the index construction and validation. The data used in this study came from households in the Montreal Urban Community (MUC) in Canada. A random sample of 1,003 English- and French-speaking heads of households adequately represents the 1.8 million persons within the MUC. An independent survey firm conducted the interviews in 1996. Results show that the weighted combined household disaster preparedness index constitutes the best construct to represent the concepts under study. Study results also reveal that risk-perception variables (attitudinal factors) offered the strongest explanatory power. Household structure and demographic variables collectively explained less than 8% of the dependent variable. The model used in this study yielded a coefficient of determination of .320, explaining 32% of the variance in the household disaster preparedness level. Concluding this study, the discussion offers implications for both disaster managers and researchers. Researchers should add to their analysis the household perspective as a complement to the organizational one. Also, it is clear that many other conceptual issues must be explored in understanding and measuring disaster preparedness. Disaster managers should base their efforts on sound research rather than on misconceptions about social behavior. Such implications can contribute to bridging the gap and also putting into practice the knowledge drawn from this growing and collective effort of studying disasters.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.023 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".