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Record W4312431843 · doi:10.33921/ahet8742

Investigating Mental Health During the COVID-19 Pandemic : A Conceptual Analysis of Thwarted Belongingness, Loneliness, and Social Isolation

2022· article· en· W4312431843 on OpenAlexaffvenue
Jan A. Wozniak

Bibliographic record

VenueJournal of Interpersonal Relations Intergroup Relations and Identity · 2022
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBelongingnessLonelinessVaguenessSocial isolationIsolation (microbiology)Social distancePsychologySocial psychologyMental healthMeaning (existential)SociologyCoronavirus disease 2019 (COVID-19)MedicineDiseasePsychotherapist

Abstract

fetched live from OpenAlex

The coronavirus disease 2019 (COVID-19) has brought unprecedented challenges to global populations since its outbreak in December 2019. Given the strict quarantine mandates, many researchers and health experts have been concerned about the unknown immediate and long-term psychological effects of physical distancing. This article investigates this phenomenon by evaluating the constructs thwarted belongingness, social isolation, and loneliness through the method of conceptual analysis. First, linguistic attention is given to the clarification of conceptual overlap, vagueness, and inconsistencies in construct meaning and application. Second, phenomenological descriptions are used to examine the congruity between psychological constructs and lived experience during the pandemic. Third, the novel inclusion of identity and the significance of space are applied to ascertain the contextual dimensions and mechanisms of quarantine measures and physical distancing. Lastly, this article concludes by discussing the valuable role that philosophy and conceptual analysis have in the field of psychology and COVID-19 research.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.399
Teacher spread0.342 · 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.

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

Citations0
Published2022
Admission routes2
Has abstractyes

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