Acceptability of a Bilingual Interactive Computerized Educational Module in a Poor, Medically Underserved Patient Population
Bibliographic record
Abstract
We evaluated the acceptability and impact of an audiovisual, bilingual, interactive computer module relating to appropriate antibiotic use. In winter 2001, adults seeking urgent care for acute respiratory infections at an inner-city urgent care clinic were invited to complete the computer module and survey (N = 296). After responding to questions about their symptoms, patients were provided information about their illness and appropriate antibiotic use, and then asked several questions about the acceptability of the module. The main outcomes, reflecting qualities known to enhance diffusion of innovations, were "learning something new about colds and flu" and trusting the computer information. Spanish-language respondents (16%) were much less likely to report prior computer experience, more likely to need help, and strongly preferred answering to a person compared with English-language respondents. In multivariable analysis, Spanish-language respondents were more likely to report learning something new (OR = 5.0; 95% CI: 2.0, 12.4) and trusting the information (OR = 2.5; 95% CI: 1.0, 6.0). We conclude that an interactive computer module was well received among a medically underserved urgent care clinic population. Benefits appear greatest among populations having the least experience with this medium.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".