The FA4CT algorithm: a new model and tool for consumers to assess and filter health information on the Internet.
Bibliographic record
Abstract
BACKGROUND: eHealth-literate consumers, consumers able to navigate and filter credible information on the Internet, are an important cornerstone of sustainable health systems in the 21(st) century. Various checklists and tools for consumers to assess the quality of health information on the Internet have been proposed, but most fail to take into account the unique properties of a networked digital environment. METHOD: A new educational model and tool for assessing information on the Internet has been designed and pilot tested with consumers. The new proposed model replaces the "traditional" static questionnaire/checklist/ rating approach with a dynamic, process-oriented approach, which emphasizes three steps consumers should follow when navigating the Internet. FA4CT (or FACCCCT) is an acronym for these three steps: 1) Find Answers and Compare [information from different sources], 2) Check Credibility [of sources, if conflicting information is provided], 3) Check Trustworthiness (Reputation) [of sources, if conflicting information is provided]. In contrast to existing tools, the unit of evaluation is a "fact" (i.e. a health claim), rather than a webpage or website. RESULTS: Formative evaluations and user testing suggest that the FA4CT model is a reliable, valid, and usable approach for consumers. CONCLUSION: The algorithm can be taught and used in educational interventions ("Internet schools" for consumers), but can also be a foundation for more sophisticated tools or portals, which automate the evaluation according to the FA4CT algorithm.
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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 | 0.023 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".