Evidence map›Paper›PMID 36901490›Full record

Trial reportInternational journal of environmental research and public health2023

Linguistic Predictors of Psychological Adjustment in Healthcare Workers during the COVID-19 Pandemic.

Marco Castiglioni, Cristina Liviana Caldiroli, Attà Negri, Gian Mauro Manzoni, Rossella Procaccia

Open access · goldFull text readRandomized Controlled Trial
In one paragraph

Trial report in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 2 pooled it
3.7field-weighted citation impact, top 7% of its field
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

9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 11 citations in OpenAlex.

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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

5 authors at 3 institutions in 1 country.

Marco CastiglioniDepartment of Human Sciences "R. Massa", University of Milano-Bicocca, 20126 Milan, Italy.
Cristina Liviana CaldiroliDepartment of Human Sciences "R. Massa", University of Milano-Bicocca, 20126 Milan, Italy.ORCID 0000-0002-1682-1896
Attà NegriDepartment of Human and Social Sciences, University of Bergamo, 24129 Bergamo, Italy.ORCID 0000-0003-3968-6444
Gian Mauro ManzoniFaculty of Psychology, eCampus University, 22060 Novedrate, Italy.
Rossella ProcacciaFaculty of Psychology, eCampus University, 22060 Novedrate, Italy.
Università degli Studi eCampus · ITUniversity of Milano-Bicocca · ITUniversity of Bergamo · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 broke out in China in December 2019 and rapidly became a worldwide pandemic that demanded an extraordinary response from healthcare workers (HCWs). Studies conducted during the pandemic observed severe depression and PTSD in HCWs. Identifying early predictors of mental health disorders in this population is key to informing effective treatment and prevention. The aim of this study was to investigate the power of language-based variables to predict PTSD and depression symptoms in HCWs. One hundred thirty-five HCWs (mean age = 46.34; SD = 10.96) were randomly assigned to one of two writing conditions: expressive writing (EW

Indexed as

COVID-19Stress Disorders, Post-TraumaticDepressionEmotional AdjustmentHealth PersonnelHumansLinguisticsMiddle AgedPandemicscognitive processingCOVID-19depression symptomsemotional processingexpressive writinghealthcare workersmaking sensepsychological adjustmentPTSD symptomsself-immersed processing

Identifiers

PMID36901490
PMCPMC10002307
OpenAlexW4323032267

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read17
Read underepoch 390

Registered trials

None linked

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.