Evidence map›Paper›PMID 39553283›Full record

ArticleDigital health

An approach to evaluation of digital data in public health campaigns.

Alan R Teo, Sean P M Rice, Elizabeth Meyer, Elizabeth Karras-Pilato, Susan Strickland, Steven K Dobscha

Abstract read
In one paragraph

Article in Digital health. 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
–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

2 citing papers in PubMed.

  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

6 authors.

Alan R TeoVA Portland Health Care System, HSR Center to Improve Veteran Involvement in Care (CIVIC), Portland, OR, USA.ORCID https://orcid.org/0000-0002-2393-088X
Sean P M RiceBiostatistics & Design Program, School of Public Health, Oregon Health & Science University-Portland State University, Portland, Oregon, USA.
Elizabeth MeyerThe District Communications Group, LLC (DCG), Washington, DC, USA.
Elizabeth Karras-PilatoCenter of Excellence for Suicide Prevention, VA Finger Lakes Health Care System, Canandaigua, NY, USA.
Susan StricklandOffice of Suicide Prevention, Department of Veterans Affairs, Washington, DC, USA.
Steven K DobschaVA Portland Health Care System, HSR Center to Improve Veteran Involvement in Care (CIVIC), Portland, OR, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mass media campaigns for public health often rely heavily on digital media and advertising tools that are customarily the domain of marketing professionals and primarily used for commercial purposes. Digital campaigns also generate a myriad of metrics, which can pose both a challenge and opportunity for scientists wishing to leverage these data for research and evaluation. Objective: The aim of this article is to provide practical guidance for the evaluation of paid media campaigns, with a focus on analyzing digital data generated directly by the campaign. Methods: Building off the Centers for Disease Control framework for program evaluation, we describe a step-by-step process for evaluation tailored to the unique considerations of digital and paid media campaigns. We contextualize our guidance with our experience evaluating a suicide prevention campaign conducted from 2021 to 2023 that focused on firearms safety in U.S. military veterans. Results: Key terminology, conceptual models, and selected findings from our evaluation are presented alongside our guidance. Conclusions: We conclude with key lessons learned and offer recommendations that are broadly applicable to evaluation of other digital campaigns.

Indexed as

diseaselethal means safetyMass media campaignpsychologypublic healthsuicideveterans

Identifiers

PMID39553283
PMCPMC11565621

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.