Bibliographic record
Abstract
This research focuses on the impact of digital search on health information, primarily health insurance and health insurance literacy (HIL). Through the use of Methods AGI, a Google search simulation software, our study examined the health insurance search of young adults 18-25 and evaluated their literacy using the health insurance literacy measure (HILM). With the marketing of AI to mass audiences and its introduction as a tool for search, this study incorporated AI into the search simulation and measured AI interaction and its impact (if any) on the HIL of the participants. Recherche numérique et littératie en matière d'assurance maladie aux États-Unis RésuméCette étude se concentre sur l'impact de la recherche numérique sur l'information médicale, principalement l'information sur l'assurance maladie et la compréhension du système d'assurance maladie. Par le biais de l'utilisation d'un simulateur du moteur de recherche Google, cette étude a examiné la recherche d'informations médicales par les jeunes adultes qui ont entre 18 et 25 ans, et a évalué leur littératie en employant l'outil de mesure de la compréhension de l'assurance maladie (HILM). En raison de la commercialisation de l'IA au grand public et de son introduction en tant qu'outil de recherche, cette étude a incorporé l'IA dans la simulation de recherche et a mesuré si celle-ci avait un impact sur le développement de la compréhension du système d'assurance maladie chez les participants. Mots-clésAssurance maladie; Connaissances sur l'assurance maladie; Information médicale, Intelligence artificielle; Évaluation de recherche
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".