Interactive Voice Response Technology as a Data Collection Tool Compared to a Household Survey: What We Learned
Bibliographic record
Abstract
The COVID-19 pandemic profoundly affected all aspects of life, including the Canadian Red Cross’s (CRC) operations. To continue delivering projects and evaluations under restrictions, the CRC adapted its methods. This adaptation is exemplified by the Adolescent Sexual and Reproductive Health and Rights project in Mali, launched during global lockdowns. Typically, such projects use standardized monitoring frameworks with pre–post-intervention measurements and household surveys for data collection. However, the pandemic made household surveys impractical. The CRC employed interactive voice response (IVR) technology to gather data, a method commonly used by telecommunications companies for short surveys. This study explores how the CRC used IVR for a more extended 45-minute questionnaire on sensitive topics like adolescent girls’ reproductive health. The article discusses the benefits and drawbacks of IVR compared to traditional household surveys, detailing the challenges faced and solutions implemented. Ultimately, the authors compare the effectiveness of both methods, offering insights into ensuring data quality in constrained circumstances. The evaluators’ perspectives on the two methodologies underscore the adaptability required to maintain robust data collection and project monitoring during unprecedented times.
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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 | 0.175 | 0.282 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".