Evidence map›Paper›PMID 35987991›Full record

ArticleBMC public health2022

The development and initial validation of a new working time scale for full-time workers with non-standard schedules.

Jennifer M Cavallari, Rick Laguerre, Jacqueline M Ferguson, Jennifer L Garza, Adekemi O Suleiman, Caitlin Mc Pherran Lombardi, Janet L Barnes-Farrell, Alicia G Dugan

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.7field-weighted citation impact, top 15% 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

2 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. 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

8 authors at 4 institutions in 1 country.

Jennifer M CavallariDepartment of Public Health Sciences, UConn School of Medicine, 263 Farmington Ave MC6325, Farmington, CT, 06030-6325, USA. cavallari@uchc.edu.
Rick LaguerreDepartment of Psychological Sciences, Division of Industrial and Organizational Psychology, University of Connecticut, Mansfield, USA.
Jacqueline M FergusonDepartment of Epidemiology and Population Health, Stanford University, Stanford, USA.
Jennifer L GarzaDepartment of Medicine, Division of Occupational and Environmental Medicine, UConn School of Medicine, Farmington, USA.
Adekemi O SuleimanDepartment of Public Health Sciences, UConn School of Medicine, 263 Farmington Ave MC6325, Farmington, CT, 06030-6325, USA.
Caitlin Mc Pherran LombardiDepartment of Human Development and Family Sciences, University of Connecticut, Mansfield, USA.
Janet L Barnes-FarrellDepartment of Psychological Sciences, Division of Industrial and Organizational Psychology, University of Connecticut, Mansfield, USA.
Alicia G DuganDepartment of Medicine, Division of Occupational and Environmental Medicine, UConn School of Medicine, Farmington, USA.
UConn Health · USUniversity of Connecticut · USMansfield University · USStanford University · US

Funding

Occupational Health Psychology TrainingT03OH008610 · OH · UNIVERSITY OF CONNECTICUT STORRS · PI MAGLEY, VICKI J · 2014 to 2024
$1.3M
NIOSH CDC HHS T03 OH008610
6 · The paper itself

Abstract

backgroundWorking time characteristics have been used to link work schedule features to health impairment; however, extant working time exposure assessments are narrow in scope. Prominent working time frameworks suggest that a broad range of schedule features should be assessed to best capture non-standard schedules. The purpose of this study was to develop a multi-dimensional scale that assesses working time exposures and test its reliability and validity for full-time workers with non-standard schedules.

methodsA cross-sectional study was conducted using full-time, blue-collar worker population samples from three industries - transportation (n = 174), corrections (n = 112), and manufacturing (n = 99). Using a multi-phased approach including the review of scientific literature and input from an advisory panel of experts, the WorkTime Scale (WTS) was created and included multiple domains to characterize working time (length, time of day, intensity, control, predictability, and free time). Self-report surveys were distributed to workers at their workplace during company time. Following a comprehensive scale development procedure (Phase 1), exploratory factor analysis (EFA) (Phase 2) and, confirmatory factor analysis (CFA) (Phase 3; bivariate correlations were used to identify the core components of the WTS and assess the reliability and validity (Phase 4) in three samples.

resultsPhase 1 resulted in a preliminary set of 21 items that served as the basis for the quantitative analysis of the WTS. Phase 2 used EFA to yield a 14-item WTS measure with two subscales ("Extended and Irregular Work Days (EIWD)" and "Lack of Control (LOC)"). Phase 3 used CFA to confirm the factor structure of the WTS, and its subscales demonstrated good internal consistency: alpha coefficients were 0.88 for the EIWD factor and 0.76-0.81 for the LOC factor. Phase 4 used bivariate correlations to substantiate convergent, discriminant, and criterion (predictive) validities.

conclusionsThe 14-item WTS with good reliability and validity is an effective tool for assessing working time exposures in a variety of full-time jobs with non-standard schedules.

Indexed as

WorkplaceCross-Sectional StudiesFactor Analysis, StatisticalHumansPsychometricsReproducibility of ResultsSurveys and QuestionnairesExtended operationIrregular shift systemNight workReliabilityScaleShift workValidityWork hours

Identifiers

PMID35987991
PMCPMC9392932
OpenAlexW4292440715

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.