A Think-Aloud Study to Understand Factors Affecting Online Health Search
Bibliographic record
Abstract
The majority of US Internet users have searched the internet for health-related information. When people conduct these health searches, searching for information about medical treatments is among the more common reasons. While being a convenient and fast method to collect information, when used by people for health search, search engines can be biased toward results saying treatments are helpful, regardless of the truth. The presence of incorrect information in search results may potentially cause harm, especially if people believe what they read without further research or professional medical advice. In this paper, we aim to better understand the decision making process of determining the efficacy of medical treatments using search result pages. We conducted a think-aloud study in order to gain insights on strategies people use during online search for health related topics. We found that, even when participants are careful and focused on the task, biased search engine results can significantly influence people to make decisions consistent with the bias. The chief reason biased search engines results were able to influence participants is that participants often considered what the majority of the search results stated as part of their decision-making. We also found that participants looked for indications of authoritativeness and quality when evaluating online content. While rank bias and a bias towards wanting treatments to be helpful has been found in prior studies, our participants did not reveal these biases as part of their spoken thoughts. Our results imply that more attention should be paid to search engines' biases given people's bias towards accepting the most common answer in the results as the correct answer. When search results are biased toward incorrect results for health-related searches, dire consequences may be the result.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".