Evidence map›Paper›PMID 32491123›Full record

ArticleRevista latino-americana de enfermagem2020

Leave of absence due to depression.

Carla Danielle Araújo Feitosa, Márcia Astrês Fernandes

Abstract read
In one paragraph

Article in Revista latino-americana de enfermagem, 2020. 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
  2. Article
  3. Sickness absenteeism among health care workers in the municipal public service of Chapecó-SC, in the period 2015-2018.Revista brasileira de medicina do trabalho : publicacao oficial da Associacao Nacional de Medicina do Trabalho-ANAMT
    Article
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

2 authors.

Carla Danielle Araújo FeitosaColégio Técnico de Bom Jesus, Universidade Federal do Piauí, Bom Jesus, PI, Brazil.
Márcia Astrês FernandesDepartamento de Enfermagem, Universidade Federal do Piauí, Teresina, PI, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveto analyze the occurrence of leave of absence due to depression among workers.

methodcensus, descriptive-analytical study, with retrospective collection. Population composed of 2,267 workers on leave due to depression with data from the Unified Benefits Information System. The independent variables were: sex, age group, income; county, origin, number of leaves of absence and type of benefit. For data analysis, descriptive statistics were used and the chi-square and Fisher's exact tests were applied. The variables that presented a value of p≤0.20 were submitted to logistic regression.

resultsthere was a predominance of females, age ≥50 years, from the capital, with income of one to two minimum wages, urban origin and single removal. Single leaves of absence occurred mainly due to a mild depressive episode and the benefit granted to the significant majority was social security sickness benefit. Among those who were on leave of absence more than once, the main cause was recurrent depressive disorder, a current mild episode and, in terms of benefit, social security sickness benefit. There was a statistical association between total time and absence from work. In logistic regression, it was found that the time ≥60 days, was 3.1 times longer in recurrent depressive disorder.

conclusionthere were an expressive quantitative number of absences due to depression, in which it was observed, especially, that the absence time remained long.

Indexed as

AdultBrazilDepressive DisorderFemaleHumansLogistic ModelsMaleMiddle AgedOccupational HealthRetrospective StudiesSick LeaveSocioeconomic FactorsYoung Adult

Identifiers

PMID32491123
PMCPMC7266636

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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.