Evidence map›Paper›PMID 41248541›Full record

ArticleJournal of public health management and practice : JPHMP

Effective Communication Training Needs Among US State and Local Public Health Professionals: Findings From the 2024 Public Health Workforce Interest and Needs Survey.

Jessica Owens-Young, Jonathon P Leider

Abstract read
In one paragraph

Article in Journal of public health management and practice : JPHMP. 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

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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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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

2 authors.

Jessica Owens-YoungAuthor Affiliations: Department of Public and Allied Health, Center for Urban Health Equity, School of Community Health and Policy, Morgan State University, Baltimore, Maryland (Dr Owens-Young); and Division of Health Policy and Management, University of Minnesota School of Public Health, Minneapolis, Minnesota (Dr Leider).ORCID 0000-0002-6199-3857
Jonathon P LeiderORCID 0000-0001-9905-476

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo assess perceptions of effective communication training needs among US state and local government public health employees and examine variations across generations and other worker characteristics.

designCross-sectional survey analysis using data from the 2024 Public Health Workforce Interests and Needs Survey.

settingState health agencies and local health departments across 48 states in the US.

participantsA total of 56 595 state and local government public health employees (37% response rate) representing 4 generations: Baby boomers (16%), Gen X (39%), millennials (38%), and Gen Z (7%).

main outcome measuresTraining needs for effective communication, defined as the combination of ability to communicate effectively across audiences and ability to communicate persuasively. Training needs were identified when respondents rated skills as moderately or very important but reported being unable to perform or at beginner level.

resultsOverall, 11% of respondents identified a training need for effective communication, including 7% with gaps in communicating effectively with different audiences and 9% in persuasive communication. Gen Z staff ranked persuasive communication as their top training need regardless of skill level. Logistic regression revealed that Communications and Policy staff (adjusted odds ratio 1.46, 95% CI 1.21-1.76) and Environmental Health staff (adjusted odds ratio 1.20, 95% CI 1.04-1.37) had higher odds of prioritizing communication training. Women and older generations were less likely to prioritize communication training compared to men and Gen Z, respectively.

conclusionsAlthough effective communication was identified as a training need, respondents did not consistently identify effective communication as their top perceived training need. Given the importance of communication to effective public health practice, training interventions should be tailored to different generations, roles, and programmatic areas.

Indexed as

CommunicationNeeds AssessmentPublic HealthAdultCross-Sectional StudiesFemaleHumansLocal GovernmentMaleMiddle AgedState GovernmentSurveys and QuestionnairesUnited StatesWorkforceeffective communicationpersuasive communicationpublic health workforcetraining needs

Identifiers

PMID41248541
PMCPMC12622253

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.