ArticleJournal of psychiatric research2021
Actual versus perceived infection rates of COVID-19: Impact on distress, behavior and disability.
Article in Journal of psychiatric research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Fear and Impact of COVID-19 Among Post-Infected Adults: Types and Associations with Quality of Life and Post-Traumatic Stress Symptoms.Journal of epidemiology and global health · 2024Article
- Risk estimation in relation to anxiety and depression for low probability negative events.Behaviour research and therapy · 2024Article
- Using what we know about threat reactivity models to understand mental health during the COVID-19 pandemic.Behaviour research and therapy · 2022Article
- Morbid Polarization: Exposure to COVID-19 and Partisan Disagreement about Pandemic Response.Political psychology · 2022Article
- Anxiety sensitivity prospectively predicts pandemic-related distress.Journal of affective disorders · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
objectiveAccurate threat appraisal is central to survival. In the case of the coronavirus pandemic, accurate threat appraisal is difficult due to incomplete medical knowledge as well as complex social factors (e.g., mixed public health messages). The purpose of this study was to evaluate the degree to which individuals accurately perceive COVID-19 infection rates and to explore the role of COVID-19 threat perception on emotional and behavioral responses both cross sectionally and prospectively.
methodsA community sample (N = 249) was assessed using online crowdsourcing and followed for one month. COVID-19 threat appraisal was compared with actual COVID-19 infection rates and deaths at the time of data collection in each participant's county and state. It was predicted that actual versus perceived COVID-19 infection rates would only be modestly associated. Relative to actual infection rates, perceived infection rates were hypothesized to be a better predictor of COVID-related behaviors, distress, and impairment.
resultsFindings indicated that relative to actual infection, perceived infection was a better predictor of COVID-related outcomes cross sectionally and longitudinally. Interestingly, actual infection rates were negatively related to behaviors cross sectionally (e.g., less stockpiling). Prospectively, these variables interacted to predict avoidance behaviors over time such that the relationship between perceived infection and avoidance was stronger as actual infection increased.
conclusionsThese data suggest that perceived COVID-19 infection is significantly associated with COVID-related behaviors, distress and impairment whereas actual infection rates have a less important and perhaps even paradoxical influence on behavioral responses to the pandemic.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.