Where's the Evidence for Evidence-based Knowledge in Ehealth Systems?
Bibliographic record
Abstract
Consumers are increasingly turning to the Internet to gather information in making a wide variety of decisions - from the reliability of different brands of automobiles, to product costs. Similarly, many individuals are making use of online sources of information through the Internet and their employers to make health-related decisions - including options in health plans and personal assistance. But is the information that is available useful or, more importantly, accurate? Could online information even be harmful? A number of studies have looked at assessing the quality of online information available to consumers and concluded that much of the information, while perhaps not incorrect, is not well-founded. Several of these studies have pointed to the need to rely more on evidence-based approaches. In this paper we argue that evidence-based approaches are needed and that evidence must form the basis for the information provided to consumers. This raises a number of challenges in both how to embody evidence within eHealth systems as well as how to validate the effectiveness of such approaches. We identify these challenges and outline research directions for overcoming them.
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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 | 0.210 | 0.572 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.025 | 0.033 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.019 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".