South American Validation of a Survey to Assess Eco-Anxiety in Adults (ECO-ANS-LATAM)
Bibliographic record
Abstract
Background: climate change is a reality, and more and more people are becoming aware of this global problem, which has generated anxiety in some populations. To validate a short survey to assess eco-anxiety in adults in South America. Methods: It is an instrumental study, and the validation was based on a previous survey, which had six questions and was generated by 217 respondents in the USA in 2021. These questions were subjected to a validation process with expert judgment, pilot and application, and then statistics were obtained. It was validated with 1907 people in six countries in South America, where the mean, standard deviation, skewness and kurtosis were adequate. Results: The initial confirmatory factorial model obtained unsatisfactory goodness-of-fit indices, so the indices were modified through a re-specification, where two items were eliminated, after which adequate values were obtained (χ2 = 22.34, df = 2, p = 0.00; RMR = 0.020; GFI = 0.990; CFI = 0.990; TLI = 0.990; and RMSEA = 0.070). Finally, the overall Cronbach’s α was calculated to be 0.88 (95% CI = 0.86–0.89). Conclusions: The test was validated in a large South American population and found that only four questions can efficiently measure anxiety about the effects of climate change. The instrument can be used with other tests to screen different age groups, ethnicities and realities.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".