Assessing Media and Science Literacy in Adults: A Scoping Review of Existing Assessment Tools
Bibliographic record
Abstract
Individual’s capacity to navigate today’s complex information landscape has become a critical issue, particularly in the context of public health, where misinformation poses significant risks. This scoping review aimed to identify and describe existing tools for assessing media and science literacies, including their definitions, conceptual frameworks, psychometric properties, and target populations. A comprehensive search of English and French publications was conducted up to July 11, 2023, across PUBMED, Embase (Ovid), PsycINFO, Cochrane Databases of Systematic Reviews, and Scopus. Of 5,910 unique references, 32 articles met the final inclusion criteria: 16 on media literacy and 16 on science literacy. Fifteen media literacy tools were identified, focusing on traditional and digital media. Most used Likert-scale items, with some incorporating open-ended responses. These tools assessed skills such as critical thinking, digital proficiency, and awareness of the socio-political context of media content. Twelve tools assessed science literacy, covering general knowledge, environmental health, and specific scientific domains. They measured cognitive, behavioural, and attitudinal dimensions using various formats, including Likert scales, multiple-choice, and open-ended items. This review highlights diverse approaches to media and science literacy assessment and calls for interdisciplinary, digitally responsive tools co-developed with educators, experts, and underrepresented communities.
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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 | 0.031 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.034 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".