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The flashbulb-like nature of memory for the first COVID-19 case and the impact of the emergency. A cross-national survey.

Tiziana LancianoFederica AlfeoAntonietta CurciClaudia MarinAngela Maria D'UggentoDiletta DecarolisSezin ÖnerKristine AnthonyKrystian BarzykowskiMiguel BascónAlec BenavidesAnne CabildoManuel Luis de la Mata-Benítezİrem ErgenKatarzyna FilipAlena GofmanSteve M J JanssenZhao Kai-BinIoanna MarkostamouJose Antonio Matías-GarcíaVeronika NourkovaSebastian OleksiakAndrés SantamaríaKarl SzpunarAndrea TaylorLynn Ann WatsonJin Zheng
Published in: Memory (Hove, England) (2024)
Flashbulb memories (FBMs) refer to vivid and long-lasting autobiographical memories for the circumstances in which people learned of a shocking and consequential public event. A cross-national study across eleven countries aimed to investigate FBM formation following the first COVID-19 case news in each country and test the effect of pandemic-related variables on FBM. Participants had detailed memories of the date and others present when they heard the news, and had partially detailed memories of the place, activity, and news source. China had the highest FBM specificity. All countries considered the COVID-19 emergency as highly significant at both the individual and global level. The Classification and Regression Tree Analysis revealed that FBM specificity might be influenced by participants' age, subjective severity (assessment of COVID-19 impact in each country and relative to others), residing in an area with stringent COVID-19 protection measures, and expecting the pandemic effects. Hierarchical regression models demonstrated that age and subjective severity negatively predicted FBM specificity, whereas sex, pandemic impact expectedness, and rehearsal showed positive associations in the total sample. Subjective severity negatively affected FBM specificity in Turkey, whereas pandemic impact expectedness positively influenced FBM specificity in China and negatively in Denmark.
Keyphrases
  • coronavirus disease
  • sars cov
  • respiratory syndrome coronavirus
  • healthcare
  • emergency department
  • machine learning
  • mental health
  • depressive symptoms