A Case Study of an SMS Text Message Community Panel Survey and Its Potential for Use During the COVID-19 Pandemic
Bibliographic record
Abstract
During the COVID-19 pandemic many traditional methods of data collection, such as intercept surveys or focus groups, are not feasible. This paper proposes that establishing community panels through SMS text messages may be a useful method during the pandemic, by describing a case study of how an innovative SMS text message community panel was used for the "Shisha No Thanks" project to collect data from young adults of Arabic-speaking background about their attitudes on the harms of waterpipe smoking. Participants were asked to complete an initial recruitment survey, and then subsequently sent 1 survey question per week. The study recruited 133 participants to the SMS text message community panel and the mean response rate for each question was 73.0% (97.1/133) (range 76/133 [57.1%] to 112/133 [84.2%]). The SMS text message community panel approach is not suited for all populations, nor for all types of inquiry, particularly due to limitations of the type of responses that it allows and the required access to mobile devices. However, it is a rapid method for data collection, and therefore during the COVID-19 pandemic, it can provide service providers and policymakers with timely information to inform public health responses. In addition, this method negates the need for in-person interactions and allows for longitudinal data collection. It may be useful in supplementing other community needs assessment activities, and may be particularly relevant for people who are considered to be more difficult to reach, particularly young people, culturally and linguistically diverse communities, and other groups that might otherwise be missed by traditional methods.
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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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".