Development and validation of five behavioral indices of flood adaptation
Bibliographic record
Abstract
BACKGROUND: In the current context of climate change, climate forecasts for the province of Quebec (Canada) are a lengthening of the thunderstorm season and an increase in episodes of intense precipitations. These changes in the distribution of precipitations could heighten the intensity or frequency of floods, a natural hazard that concerns 80% of Quebec's riverside municipalities. For the health and safety of the at-risk population, it is very important to make sure they have acquired necessary adaptive behaviors against flooding hazard. However, there has been no assessment of these flood adaptation behaviors to date. Thus, the aim of this study was to develop and validate five indices of adaptation to flooding. METHODS: A sample of 1951 adults completed a questionnaire by phone. The questionnaire, specifically developed for this study, measured whether they did or did not adopt the behaviors that are proposed by public health officials to protect themselves against flooding. RESULTS: The results of the item, confirmatory factor, and multiple correspondence analyses contributed to the development of five indices corresponding to the adaptation behaviors to adopt according to the chronology of events: (a) pre-alert preventive behaviors, (b) behaviors to carry out after the alert is issued, (c) behaviors to adopt during a flood not requiring evacuation, (d) behaviors to adopt during a flood requiring evacuation, and (e) post-flood behaviors. The results of this study also showed that people who perceive a risk of flooding in their home in the next 5 years tend to adopt more preventive behaviors and adaptation behaviors than those who perceive little or no risk at all. They also reveal that people who feel more adverse effects on their physical or mental health tend to adopt more adaptive behaviors than those who feel little or no adverse effects on their health. CONCLUSION: Across a series of psychometric analyses, the results showed that these flood adaptation indices could properly measure a vast range of adaptive behaviors according to the chronology of events. Therefore, researchers, public health agencies, and professionals can use them to monitor the evolution of individuals' adaptive behaviors during floods.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".