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Record W4412514290 · doi:10.1386/fspc_00348_1

Coping with fashion for mental health during the COVID-19 pandemic: Part II on motivations and effects

2025· article· en· W4412514290 on OpenAlexaffabout
Malgosia Wenderski, Jaehee Jung

Bibliographic record

VenueFashion Style & Popular Culture · 2025
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicCoping (psychology)Mental health2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PsychologyMedicinePsychiatryVirologyInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

Understanding clothing’s role in coping and managing daily needs and stressors is critical in understanding its therapeutic possibilities and effects on mental health. Part II of the study involved 576 Canadian emerging adults, exploring their motivations behind clothing engagement whilst navigating the peaks of the pandemic, and how clothing affected their mental health. Individuals reported multiple motivations in their use of clothing, including comfort, self-assurance, individuality, fashion, camouflage, motivation and normalcy. The quantitative analysis found no relationship between the practice of enhancing mood through clothing and mental health. The qualitative analysis revealed several positive and negative effects clothing had on mental health through self-esteem, mood, self-efficacy, distraction, self-congruency and self-empowerment. Findings provide partial evidence that clothing practices and COVID-19’s disruptions to clothing engagement effected perceived mental health. Future research is encouraged to further explore the relationship between clothing and mental health.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.597
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.030
GPT teacher head0.348
Teacher spread0.319 · 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
Published2025
Admission routes2
Has abstractyes

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