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Record W3193407168 · doi:10.3389/fpsyg.2021.714749

Psychosocial Impact of COVID-19 Pandemic in Libya: A Cross-Sectional Study

2021· article· en· W3193407168 on OpenAlexaff
Alhadi M. Jahan, Marwa Sayed Mohamed, Mohammed Alfagieh, Nehal Alnawy, Muhayman Alsabiri, Rayan Algazal, Rana Saaleh, Safa El Swisy, Orjwan Abbas, Wesal Al Delawi, Butaina Abdulhafith, Osama Almangoush, Fathalla Elhag, Abdulbasit Elshukri, Wesam Abushaala, Taqwa Shahrani, Ahmed Tnton, Heba Alkilani, Amaal Dier

Bibliographic record

VenueFrontiers in Psychology · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPerceived Stress ScalePreparednessPandemicPsychosocialPsychologyCross-sectional studyMental healthDepression (economics)PopulationPublic healthScale (ratio)Sleep disorderDemographyGerontologyMedicineEnvironmental healthCoronavirus disease 2019 (COVID-19)PsychiatryStress (linguistics)InsomniaDiseaseGeography

Abstract

fetched live from OpenAlex

Background:Since the outbreak of COVID-19 were announced in Wuhan, China, the virus has spread in most countries. After one year of restrictive measures applied by governments, it is unclear how this prolonged social distancing has affected the mental health of individuals in Libya. Therefore, the present study aims to assess the levels of perceived stress, depression, sleep disturbance, and self-efficacy associated with the pandemic in Libya and their association with the demographic factors. Methods:A cross-sectional study was conducted between October 10 and November 10, 2020 in 21 cities in Libya using an online survey. The survey collected socio-demographic variables and other important psychological variables using valid scales: namely, the Perceived Stress scale, the PROMIS Depression scale, the PROMIS Sleep Disturbance scale, and self-efficacy 6-item scale. Additionally, data were collected regarding eating and smoking habits, housing and living situations, and the preparedness of the public to manage the upcoming waves of the pandemic. Results:The questionnaire was accessed 746 times, and a total of 683 completed questionnaires were analyzed (response rate of 91.6%), with ages ranging from 18 to 94 years (Mean ± SD = 27.09 ± 10.57). Among the respondents, 58.4% were females and 77% were from the age group 18–29 years. The perceived stress, depression, and sleep disturbance symptoms were high in overall population. For stress, 52.7 and 17.1% of respondents reported moderate and high stress, and for sleep quality 28.8 and 8.1% reported moderate and high sleep disturbance, respectively. For depression, the overall median score was 20 (out of 40). The perceived stress, depression, and sleep disturbance were more prevalent among females and the younger age groups (18–29 years old). The overall self-efficacy median total score was 6.67 (out of 10), with a significantly higher median total score for males than females (7 vs. 6.33,p= 0.001). About 14.5% of the respondents were regular cigarette smokers, and most of them (79.8%) described their smoking habits during the previous month as more than typical. In addition, the reported eating habits of almost half of the respondents (43%) had changed, with about one-third (29.6%) reporting that their eating habits had become less healthy during the pandemic. Conclusion:The findings of this research suggest increased levels of stress, depression, and sleep disturbances as well as COVID-19-related fear during the pandemic, especially among young females. This alarming finding urgently calls for safe and low-barrier interventions to help mentally burdened individuals. This study makes a significant contribution in providing essential data on the psychological and social impacts on the Libyan population due to 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.099
GPT teacher head0.519
Teacher spread0.419 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2021
Admission routes1
Has abstractyes

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