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Record W4381985105 · doi:10.4236/psych.2023.145040

Social Media and COVID-19: A Mixed-Methods Analysis to Document Canadian Adults’ Perceptions of the Positive and Negative Sides of Their Social Media Use

2023· article· en· W4381985105 on OpenAlexaffabout
Malinda Desjarlais

Bibliographic record

VenuePsychology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsMount Royal University
Fundersnot available
KeywordsSocial mediaPsychologyLikert scaleSocial distanceSocializationSocial psychologyPerceptionDescriptive statisticsEntertainmentMultimethodologyCoronavirus disease 2019 (COVID-19)Developmental psychology

Abstract

fetched live from OpenAlex

Using a mixed-methods approach, the purpose of this study was to document some Canadian adults’ social media experiences during the COVID-19 pandemic to understand potential changes in motives and attitudes related to social media. Participants (n = 68; 17 - 66 years old) completed an online survey with open-ended and Likert-scale questions between April 25 and June 12, 2020. Qualitative responses were coded and analyzed for themes related to the positive and negative aspects of social media use and changes in attitudes. Descriptive statistics, ANOVAs, and t-tests were run to assess perceived changes in frequency and motivations driving social media use, in addition to the perceived utility of social media. Participants perceived increases in their social media use, particularly to meet socialization and entertainment needs. During the pandemic, participants valued social media for its opportunity to maintain connections, but also expressed concerns about how much they were using it and their exposure to negative information. The results suggest that maximizing the potential of social media to maintain or increase connections may be beneficial for the well-being of some Canadians in early and middle adulthood during unprecedented times of physical distancing.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score0.915

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.426
Teacher spread0.387 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

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