Evidence map›Paper›PMID 38995756›Full record

ArticleWork (Reading, Mass.)2024

Who do you ask? Predicting injury rates from survey responses.

Leslie I Boden, Yixin Pan, Matt Gregas, Melissa McTernan, Susan E Peters, Div Bhagia, Gregory R Wagner, Erika L Sabbath

Abstract read
In one paragraph

Article in Work (Reading, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Leslie I BodenDepartment of Environmental Health, Boston University School of Public Health, Boston, MA, USA.
Yixin PanAcademic Research Services, Boston College, Chestnut Hill, MA, USA.
Matt GregasAcademic Research Services, Boston College, Chestnut Hill, MA, USA.
Melissa McTernanAcademic Research Services, Boston College, Chestnut Hill, MA, USA.
Susan E PetersDepartment of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Div BhagiaDepartment of Economics, California State University, Fullerton, CA, USA.
Gregory R WagnerHarvard Center for Work, Health, and Wellbeing, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Erika L SabbathDepartment of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Funding

The Harvard TH Chan School of Public Health Center for Work, Health and WellbeingU19OH008861 · OH · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI SORENSEN, GLORIAN C · 2007 to 2025
$23.9M
NIOSH CDC HHS U19 OH008861
6 · The paper itself

Abstract

backgroundFew studies have addressed whether using manager or worker perceptions of safety policies and practices alone predict reported injury rates less accurately than using both.

objectiveThis study provides an example and describes a method that can be used to address this issue with survey instruments designed to measure safety climate, policies, or practices.

methodsUsing multilevel logistic regression, we estimated the relationship between worker and manager perceptions of a given exposure and the odds of worker injury during the post-survey year for three safety scales. We tested whether surveying both workers and managers provides additional predictive value compared with surveying just one group.

resultsInjury in the year following the survey was significantly associated with worker scores on two of the three scales. Manager responses were not significantly associated with injury and did not significantly improve injury rate prediction when added to a model with only worker survey responses.

conclusionsThe capacity of manager-only or worker-only perceptions of safety policies and practices to predict worker injuries should be established before choosing to survey just one or the other. The approach and findings in this paper can be applied to other survey instruments and in other settings to help make this choice.

Indexed as

Occupational InjuriesAccidents, OccupationalAdultFemaleHumansLogistic ModelsMaleMiddle AgedOccupational HealthPerceptionSafety ManagementSurveys and Questionnairesinjury preventionoccupational safety and healthsafety policies and practicesSurvey research

Identifiers

PMID38995756
PMCPMC13048218

What OpenQuestion holds

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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.