The Effect of a Disastrous Flood on the Quality of Life in Dongting Lake Area in China
Bibliographic record
Abstract
We carried out an epidemiological study to assess the impact of flood on the quality of life (QOL) of residents in the affected areas in China. We used a natural experiment approach, randomly selected 494 adults from 18 villages, which suffered from flooding as a result of embankments collapsing, 473 adults from 16 villages, which suffered from, soaked flood, and 773 adults from 11 villages without flood (control group). We used the Generic QOL Inventory-74 (GQOLI-74), social support scale, and questionnaires to assess the QOL of all study participants. The QOL was significantly poorer in soaked group (58.4) and (especially) in collapsed group (55.1) than in control group (59.5, p<0.001). Adjustment for potential confounding factors did not change the results. The impact of flood on QOL was stronger among farmers, seniors, persons with introvert personality, and residents with adverse life-events, whereas social support and extrovert personalities offset the negative impact of flood on QOL.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".