Quantifying Psychosocial Impacts From Coastal Hazards for Cost-Benefit Analysis in Eastern Quebec, Canada
Bibliographic record
Abstract
The assessment of psychosocial impacts related to coastal hazards (erosion, submersion) has so far been mainly qualitative. As cost-benefit analysis is gaining popularity among communities to assess adaptation options in the face of increasing coastal hazards, there is a need to develop quantitative indicators to improve the inclusion of human impacts in decision-making. The project therefore aimed to suggest quantitative indicators for a cost-benefit analysis in the Lower St. Lawrence region exposed to the waters of the estuary of the St. Lawrence River in eastern Quebec, Canada. A systematic survey of five municipalities was conducted in 2019 ( n = 101). In general, the prevalence of mental health impacts was the double than that of physical health (30 vs. 14%); and was higher for affected respondents: 50.0 and 23.9%, against 13.5 and 5.8% for unaffected respondents. With regards to psychosocial impacts, the main results were that affected people were 2.33 more stressed in normal times than unaffected respondents and this variation increased to 3.54 during a storm surge warning; the quality of sleep of affected respondents when a storm warning is issued was 2.39 poorer than that of unaffected respondents. With regards with economic impacts, an additional 11% in absenteeism has been observed among respondents affected; the likelihood of experiencing financial difficulties was 1.27 higher for those affected; a small subgroup of affected respondents (<10) declared a mean of 400 CAD of additional health expenses. The results show that the assistance received provides little protection against stress, or even increases it, if it is mainly financial. In addition, a high degree of social isolation and living alone increases stress in the face of hazards. Thus, social capital and psychosocial assistance act as a protective factor in reducing psychosocial impacts. The probability of financial stress, on the other hand, increases in the event of maladaptation (inefficient adaptation expenditures leading to repair costs). Overall, the importance of the impacts measured justifies further economical investigation for their inclusion in the cost-benefit analysis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".