Evidence map›Paper›PMID 37213555›Full record

ArticleData in brief2023

A dataset on the prevalence and associated risk factors for mental health problems among female university students in Bangladesh.

Zabun Nahar, Saba Eqbal, Kaniz Farzana Supti, A H M Nazmul Hasan, A B M Riaz Kawsar, Md Rabiul Islam

Abstract read
In one paragraph

Article in Data in brief, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Zabun NaharDepartment of Pharmacy, University of Asia Pacific, 74/A Green Road, Farmgate, Dhaka-1205, Bangladesh.
Saba EqbalDepartment of Pharmacy, University of Asia Pacific, 74/A Green Road, Farmgate, Dhaka-1205, Bangladesh.
Kaniz Farzana SuptiDepartment of Pharmacy, University of Asia Pacific, 74/A Green Road, Farmgate, Dhaka-1205, Bangladesh.
A H M Nazmul HasanDepartment of Pharmacy, University of Asia Pacific, 74/A Green Road, Farmgate, Dhaka-1205, Bangladesh.
A B M Riaz KawsarDepartment of Cardiology, National Institute of Cardiovascular Diseases, Sher-e-Bangla Nagar, Dhaka-1207, Bangladesh.
Md Rabiul IslamDepartment of Pharmacy, University of Asia Pacific, 74/A Green Road, Farmgate, Dhaka-1205, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The data presented here relate to the article with the following title, "Prevalence and associated risk factors for mental health problems among female university students during COVID-19 pandemic: A cross-sectional study findings from Dhaka, Bangladesh" [1]. This article provides a dataset on the prevalence of psychological distress among 451 female university students during the ongoing COVID-19 pandemic. We collected their responses from October 15, 2021, to January 15, 2022, using Google survey tools (Google Forms). A structured questionnaire was prepared, consisting of sociodemographic variables and their association with mental health problems. Three psychometric scales, UCLA-3, GAD-7, and PHQ-9, were applied to measure disorders of loneliness, anxiety, and depression, respectively. We performed the statistical analysis using IBM SPSS (v. 25.0). Each respondent gave their electronic consent for the study, and anonymized data were published. Hence, policymakers of government and non-government groups may utilize the data to create a variety of initiatives to support the mental health of female university students from Dhaka, Bangladesh.

Indexed as

AnxietyBangladeshCOVID-19DepressionLonelinessMental healthStudents

Identifiers

PMID37213555
PMCPMC10197013

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.