Examining Community Capacity and Resilience Post-Outbreak in Walkerton, Ontario
Bibliographic record
Abstract
Most disaster management studies only assess community resilience immediately following the event with no further follow-up. Accordingly, there is a lack of research being conducted to determine whether communities truly recover over time after a disaster strikes. Thus, the purpose of this research was to examine the different factors and dimensions that facilitate or hinder community resilience more than a decade post-disaster using present day Walkerton, Ontario (16 years after the effects of the 2000 water contamination outbreak). This exploratory study utilized an interpretive description qualitative methodology. Semi-structured interviews and focus groups were conducted with a purposeful sample of 29 Walkerton community members. The data were transcribed verbatim and coded using conventional content analysis to identify themes inductively. Several barriers and enabling factors were identified in maintaining community resilience under non-crisis conditions in the community. A conceptual model was developed based on the study’s findings to demonstrate the application of the life course approach within an existing community resilience framework. This model contributes to the field of disaster management in demonstrating the various ways that a disaster affects the subsequent life course of individuals post-disaster. It highlights the need to integrate a community-centred approach in disaster management to yield more effective and efficient mitigation, preparation, response, and recovery strategies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".