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Record W4393484049 · doi:10.1177/21582440241240201

Role of Media Consumption, Governmental Distrust & Psychological Vulnerability in Predicting Affective Well-being of University Students & Healthcare Professionals during COVID-19

2024· article· en· W4393484049 on OpenAlexaff
Gulnaz Anjum, Mudassar Aziz

Bibliographic record

VenueSAGE Open · 2024
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsKwantlen Polytechnic University
FundersUniversitetet i Oslo
KeywordsDistrustCoronavirus disease 2019 (COVID-19)Consumption (sociology)PsychologyHealth careVulnerability (computing)2019-20 coronavirus outbreakHealth professionalsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PandemicSocial psychologySociologyMedicineEconomicsVirologyPsychotherapistInternal medicineEconomic growthSocial science

Abstract

fetched live from OpenAlex

The present research aimed to explore the role of media consumption, governmental distrust, and psychological vulnerability in predicting the practical well-being of university students and healthcare professionals during COVID-19. Two correlational studies were conducted. Study 1 was conducted with 411 university students (206 Women; 205 men), and it was conducted during the first lockdown in Pakistan. Study 2 was conducted during the thigh-intensity phase he COVID-19, and the sample comprised 375 healthcare professionals (198 women; 177 men). Both studies showed that higher levels of media consumption, governmental distrust, and psychological vulnerability were associated with lower levels of well-being. Our path models in both studies (with students and healthcare professionals) indicate that during the pandemic, participants’ level of media consumption, trust in the government, and their personal vulnerability were negatively associated with their affective well-being. These findings have implications for individuals’ affective well-being during healthcare crises such as the COVID-19 pandemic.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.005
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.057
GPT teacher head0.459
Teacher spread0.402 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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