Cited by 120COVID-19 and Mental HealthCommunication and COVID-19 ImpactImpact of Technology on Adolescents

What Media Helps, What Media Hurts: A Mixed Methods Survey Study of Coping with COVID-19 Using the Media Repertoire Framework and the Appraisal Theory of Stress

Open access
Pahayahay, Amber, Khalili-Mahani, Najmeh·2020·Journal of Medical Internet Research

Published version of a preprint

Abstract

BACKGROUND Social and physical distancing in response to the coronavirus disease (COVID-19) pandemic has made screen-mediated information and communication technologies (media) indispensable. Whether an increase in screen use is a source of or a relief for stress remains to be seen. OBJECTIVE In the immediate aftermath of the COVID-19 lockdowns, we investigated the relation between subjective stress and changes in the pattern of media use. Based on Lazarus’s transactional model of appraisal and coping, and building on an earlier similar survey, we hypothesize that individual differences in the appraisal of media predict variations in approach or avoidance of media for coping with COVID-19 stress. METHODS Between March 20 and April 20, 2020, a brief snowball survey entitled: “What media helps, what media hurts: coping with COVID19 through screens” was distributed via Concordia University’s mailing lists and social media (PERFORM Centre, EngAGE Centre, and Media Health Lab). Using a media repertoire method, we asked questions about preferences, changes in use, and personal appraisal of media experiences (approach, avoid, and ignore) as a result of the COVID-19 pandemic and investigated interindividual differences in media use by factors such as subjective stress, age, gender, and self-reported mental health. RESULTS More than 90% of the survey respondents were in Canada and the east coast of the United States. From 685 completed responses, 169 respondents were “very stressed” and 452 were “slightly worried” about the pandemic. COVID-19 stress led to increased use of Facebook (χ23=11.76, P=.008), television (χ23=12.40, P=.006), YouTube (χ23=8.577, P=.04), and streaming services such as Netflix (χ23=10.71, P=.01). Respondents who considered their mental health “not good” were twice as likely to prefer streaming services as a coping tool for self-isolation. Women and nonbinary respondents were twice as likely than men to pick social media for coping. Individuals younger than 35 years were 3 times more likely to pick computer games, and individuals older than 55 years were more likely to pick network television or print media. Gender affected the appraisal of media (less in men than others) in terms of avoid (F1,637=5.84, P=.02) and approach scores (F1,637=14.31, P<.001). Subjective mental health affected the ignore score (less in those who said “good” than others; F1,637=13.88, P<.001). The appraisal score and use increase explained variations in worrying about physical and mental health stress due to increased screen time. A qualitative analysis of open-ended questions revealed that media (especially social networks) were important for coping if they provided support and connection through the dissemination of factual and positive information while avoiding the overflow of sensational and false news. CONCLUSIONS The relationship between appraisal of media’s positive and negative facets vary with demographic differences in mental health resiliency. The media repertoire approach is an important tool in studies that focus on assessing the benefits and harms of screen overuse in different populations, especially in the context of the COVID-19 pandemic.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.2196/20186

Cites

43 of 43 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Pahayahay, A., & Khalili-Mahani, N.
Affiliations
University of Waterloo (CA)
Venue
Journal of Medical Internet Research
Published
2020-08-06
Volume
22
Issue
8
Pages
e20186
ISSN
1438-8871 (ISSN-L)
OpenAlex
W3044753138
Cited by
120
References
43
Citations by year
2026: 3 · 2025: 5 · 2024: 15 · 2023: 16 · 2022: 37 · 2021: 42 · 2020: 2
FWCI
9.71
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Medical Internet Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Concordia University
Topics
  • Social Sciences › Psychology › Clinical Psychology › COVID-19 and Mental Health
  • Social Sciences › Communication › Communication and COVID-19 Impact
  • Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
Keywords
media repertoires, COVID-19 pandemic, stress coping, media use, social media, streaming services, television, video games, gender differences, age differences, mental health, self-isolation, lockdown, media avoidance, screen use, information seeking
MeSH terms
Betacoronavirus, Screen Time, COVID-19, SARS-CoV-2, Adaptation, Psychological, Adult, Female, Humans, Male, Mass Media, Mental Health, Middle Aged, Models, Psychological, Pneumonia, Viral, Surveys and Questionnaires, Television, Exercise, Coronavirus Infections, Video Games, Young Adult, Pandemics, Social Networking, Social Media, Geographic Mapping

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review