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Record W4390700188 · doi:10.1080/01488376.2023.2299265

Exploring the Psychosocial Impacts on COVID-19 Survivors: A Qualitative Study of Life after COVID-19 Diagnosis and Quarantine in South Korea

2024· article· en· W4390700188 on OpenAlexaff
Min Ah Kim, Jaehee Yi, Jimin Sung, Gaben Sanchez

Bibliographic record

VenueJournal of Social Service Research · 2024
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsThematic analysisPsychosocialCoronavirus disease 2019 (COVID-19)PandemicQuarantineEmpowermentQualitative researchPsychologyMedicineMental healthGerontologyPsychiatrySociologyPolitical scienceDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

COVID-19 infection and subsequent quarantine experiences during the pandemic may have profoundly affected many aspects of life among COVID-19 survivors. However, limited research has explored the changes these survivors experienced once they returned to their daily lives. This study aimed to explore COVID-19 survivors’ psychosocial impacts and their lives following infection and social quarantine during the initial phase of the pandemic in South Korea. Semistructured telephone interviews were conducted in June 2021, involving 15 COVID-19 survivors. All participants contracted COVID-19 between February 2020 and April 2021 in South Korea. This study used a qualitative methodology from phenomelogical perspectives to explore and understand the participants’ shared lived experiences. Thematic analysis identified four overarching themes and 12 subthemes among Korean COVID-19 survivors: (a) self-concept transformation; (b) changed relationship dynamics; (c) life perspective shifts; and (d) health awareness evolution. Recognizing the life changes experienced by COVID-19 survivors can inform the development of targeted peer support services and person-centered education. Focusing on health awareness and empowerment, these initiatives can promote personal growth, facilitate positive changes, and alleviate traumatic experiences for future survivors of diverse infectious diseases. Future research could investigate the long-term impact of COVID-19 on individuals’ lives and explore the mechanisms underlying these effects.

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)0.012
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.470
GPT teacher head0.585
Teacher spread0.115 · 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 designQualitative
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

Citations2
Published2024
Admission routes1
Has abstractyes

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