How older adults manage misinformation and information overload - A qualitative study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic was characterized by an abundance of information, some of it reliable and some of it misinformation. Evidence-based data on the impact of misinformation on attitudes and behaviours remains limited. Studies indicate that older adults are more likely to embrace and disseminate misinformation than other population groups, making them vulnerable to misinformation. The purpose of this article is to explore the effects of misinformation and information overload on older adults, and to present the management strategies put in place to deal with such effects, in the context of COVID-19. METHODS: A qualitative exploratory approach was adopted to conduct this research. A total of 36 semi-structured interviews were conducted with older adults living in Quebec, Canada. The interviews were fully transcribed and subjected to a thematic content analysis. RESULTS: Participants said they could easily spot misinformation online. Despite this, misinformation and its treatment by the media could generate fear, stress and anxiety. Moreover, the polarization induced by misinformation resulted in tensions and even friendship breakdowns. Participants also denounced the information overload produced largely by the media. To this end, the participants set up information routines targeting the sources of information and the times at which they consulted the information. CONCLUSIONS: This article questions the concept of vulnerability to misinformation by highlighting older adults' agency in managing misinformation and information overload. Furthermore, this study invites us to rethink communication strategies by distinguishing between information overload and misinformation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.014 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".