Telephone versus web panel National Survey for monitoring adoption of preventive behaviors to climate change in populations: a case study of Lyme disease in Québec, Canada
Bibliographic record
Abstract
BACKGROUND: To monitor the adoption of climate change adaptive behaviors in the population, public health authorities have to conduct national surveys, which can help them target vulnerable subpopulations. To ensure reliable estimates of the adoption of these preventive behaviors, many data collection methods are offered by polling firms. The aim of this study was to compare a telephone survey with a web survey on Lyme disease with regard to their representativeness. METHODS: The data comes from a cross-sectional study conducted in the Province of Québec (Canada). In total, 1003 people completed the questionnaire by telephone and 956 filled in a web questionnaire. We compared the data obtained from both survey modes with the census data in regard to various demographic characteristics. We then compared the data from both samples in terms of self-reported Lyme disease preventive behaviors and other theoretically associated constructs. We also assessed the measurement invariance (equivalence) of the index of Lyme disease preventive behaviors across the telephone and web samples. RESULTS: Findings showed that neither the telephone nor the web panel modes of data collection can be considered more representative of the target population. The results showed that the proportion of item non-responses was significantly higher with the web questionnaire (5.6%) than with the telephone survey (1.3%), and that the magnitude of the differences between the two survey modes was nil for 19 out of the 30 items related to Lyme disease, and small for 11 of them. Results from invariance analyses confirmed the measurement invariance of an index of adaptation to Lyme disease, as well as the mean invariance across both samples. CONCLUSIONS: Our results suggested that both samples provided similar estimates of the level of adaptation to Lyme disease preventive behaviors. In sum, the results of our study showed that neither survey mode was superior to the other. Thus, in studies where adaptation to climate change is monitored over time, using a web survey instead of a telephone survey could be more cost-effective, and researchers should consider doing so in future surveys on adaptation to climate. However, we recommend conducting a pretest study before deciding whether to use both survey modes or only one of them.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".