Assessing the Long-Term Transitional Impact and Mental Health Consequences of the Southern Alberta Flood of 2013
Bibliographic record
Abstract
Natural disasters pose an increasing threat to individuals and their well-being. Although much is known about the short-term effects of a disaster, there has been much less work on how disasters affect individuals over long periods. Additionally, disaster research has traditionally focused either on the mental outcome or economic impacts, limiting the understanding of the link between disaster-induced changes (i.e., transition) and mental health. Thus, this exploratory study aimed to measure the long-term transitional impacts of the Southern Alberta flood of 2013 and the relationship between this disaster-specific transition and well-being. In this follow-up, conducted six years after the flood, 65 participants were re-assessed on the 12-item Transitional Impact Scale (TIS-12) and their ratings were compared across two-time points (2013 vs. 2019). Additionally, the 21-item DASS and the 8-item PCL-5 were introduced in the follow-up to assess these participants’ mental health states. Paired T-tests of the material and psychological subscale of the TIS demonstrated significantly lower ratings in 2019 than in 2013. After six years, PTSD had a high correlation with the material and psychological subscale of the TIS and DASS. However, depression and anxiety were reliably related to psychological TIS only. Overall, the findings suggest that individuals’ well-being is largely determined by the level of disaster-related material and psychological life changes experienced over time. These findings might be useful to take note of the short-term and long-term impact of disaster-specific transitions while assisting professionals and policymakers in formulating interventions to preserve people’s well-being during the disaster and promote resilience following it.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".