Evidence map›Paper›PMID 40224585›Full record

ArticleDepression and anxiety2023

Trust in Medical Professionals Moderates Depression in Hong Kong during COVID-19.

Daniel W L Lai, Jiahui Jin, Elsie Yan, Vincent W P Lee

Abstract read
In one paragraph

Article in Depression and anxiety, 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

4 authors.

Daniel W L LaiFaculty of Social Sciences, Hong Kong Baptist University, Hong Kong.ORCID 0000-0001-6365-3189
Jiahui JinWee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore.ORCID 0000-0002-7832-0894
Elsie YanDepartment of Applied Social Sciences, The Hong Kong Polytechnic University, Hong Kong.ORCID 0000-0002-0604-6259
Vincent W P LeeFaculty of Social Sciences, Hong Kong Baptist University, Hong Kong.ORCID 0000-0003-4574-636X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Given the continuing COVID-19 pandemic and the associated prevention and control measures implemented, the psychological burden brought by the pandemic on citizens is expected to increase. This study is aimed at exploring the predictors of depressive symptoms among Hong Kong people during the epidemic, as well as factors that could potentially alleviate the negative effects of the epidemic. Methods: The third wave follow-up survey (December 2021 to January 2022) from a longitudinal prospective survey study conducted in Hong Kong was used for a cross-sectional analysis. The participants ( Results: With minimized confounding effects of demographic variables, higher levels of concern about infection, experience with COVID-19 infection and previous epidemics, hassles, and trust in authority increased the odds of being depressed, while a higher level of trust in medical professionals reduced the odds of depression. Moreover, greater trust in medical professionals, as a moderator, lessened the positive associations between the levels of depression and hassles and concern about infection. Conclusions: Even though the threats of COVID-19 seem to have lowered, this study shows that a few factors associated with the pandemic continue to threaten people's mental health. However, developing greater trust in medical experts may be an effective way to relieve psychological burden.

Indexed as

COVID-19DepressionHealth PersonnelTrustAdolescentAdultAgedCross-Sectional StudiesFemaleHong KongHumansLongitudinal StudiesMaleMiddle AgedProspective StudiesYoung Adult

Identifiers

PMID40224585
PMCPMC11921831

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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.