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Record W3196531884 · doi:10.2196/28002

“Skip the Small Talk” Virtual Event Intended to Promote Social Connection During a Global Pandemic: Online Survey Study

2021· article· en· W3196531884 on OpenAlexvenueno aff
Jasmine Mote, Kathryn Gill, Daniel Fulford

Bibliographic record

VenueJMIR Formative Research · 2021
Typearticle
Languageen
FieldPsychology
TopicMental Health via Writing
Canadian institutionsnot available
Fundersnot available
KeywordsLonelinessSocial distancePsychologyAffect (linguistics)Event (particle physics)PandemicSocial mediaCoronavirus disease 2019 (COVID-19)Social psychologyMedicineComputer scienceWorld Wide WebCommunication

Abstract

fetched live from OpenAlex

BACKGROUND: Social distancing measures meant to prevent the spread of COVID-19 in the past year have exacerbated loneliness and depression in the United States. While virtual tools exist to improve social connections, there have been limited attempts to assess community-based, virtual methods to promote new social connections. OBJECTIVE: In this proof-of-concept study, we examined the extent to which Skip the Small Talk (STST)-a business dedicated to hosting events to facilitate structured, vulnerable conversations between strangers-helped reduce loneliness in a virtual format in the early months of the 2020 COVID-19 pandemic. We predicted that participants who attended STST virtual events would show a reduction in loneliness, improvement in positive affect, and reduction in negative affect after attending an event. We were also interested in exploring the role of depression symptoms on these results as well as the types of goals participants accomplished by attending STST events. METHODS: Adult participants who registered for an STST virtual event between March 25 and June 30, 2020, completed a survey before attending the event (pre-event survey; N=64) and a separate survey after attending the event (postevent survey; n=25). Participants reported on their depression symptoms, loneliness, and positive and negative affect. Additionally, participants reported the goals they wished to accomplish as well as those they actually accomplished by attending the STST event. RESULTS: The four most cited goals that participants hoped to accomplish before attending the STST event included the following: "to make new friends," "to have deeper/better conversations with other people," "to feel less lonely," and "to practice social skills." A total of 34% (20/58) of participants who completed the pre-event survey reported depression symptoms that indicated a high risk of a major depressive episode in the preceding 2 weeks. Of the 25 participants who completed the pre- and postevent surveys, participants reported a significant reduction in loneliness (P=.03, Cohen d=0.48) and negative affect (P<.001, Cohen d=1.52) after attending the STST event compared to before the event. Additionally, depressive symptoms were significantly positively correlated with change in negative affect (P=.03), suggesting that the higher the depression score was prior to attending the STST event, the higher the reduction in negative affect was following the event. Finally, 100% of the participants who wished to reduce their loneliness (11/11) or feel less socially anxious (5/5) prior to attending the STST event reported that they accomplished those goals after the event. CONCLUSIONS: Our preliminary assessment suggests that the virtual format of STST was helpful for reducing loneliness and negative affect for participants, including those experiencing depression symptoms, during the COVID-19 pandemic. While encouraging, additional research is necessary to demonstrate whether STST has benefits when compared to other social events and interventions and whether such benefits persist beyond the events themselves.

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)0.005
metaresearch head score (Gemma)0.001
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 score0.528
Threshold uncertainty score0.802

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.257
GPT teacher head0.543
Teacher spread0.287 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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