Community social capital and individual disaster preparedness in immigrants and Canadian-born individuals: an ecological perspective
Bibliographic record
Abstract
Psychological research on the predictors of disaster preparedness has mainly focused on individual-level factors, although the social environment plays an important role. Our goal is to provide a systemic perspective to help improve risk communication and risk management for natural disaster risks. We examined how community-level social capital related to individual-level disaster preparedness in immigrants compared with Canadian-born individuals. We characterised participants’ communities’ social capital by conceptually linking two national surveys using postal codes. We performed sequential linear multiple regression analysis to examine the relationship between community social capital and individual disaster preparedness. Results revealed three components of social capital: societal trust, interaction with friends, and neighbourhood contact. Societal trust positively predicted the extent to which immigrants and Canadian-born individuals knew someone who would search for them post-disaster. Interestingly, results revealed that Canadian-born individuals were more likely to uptake emergency planning when living in a community with strong societal trust, while the reverse was true for immigrants. Results suggest that some components of social capital may have an effect on certain preparedness behaviours. Societal trust could have both positive and negative effects on emergency planning depending on individuals’ immigrant status. Risk communication and risk management should consider social capital as part of the framework for effective disaster preparedness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".