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Record W2017674697 · doi:10.2196/resprot.2713

Purposive Facebook Recruitment Endows Cost-Effective Nutrition Education Program Evaluation

2013· article· en· W2017674697 on OpenAlexvenueno aff
Barbara Lohse, Patricia Wamboldt

Bibliographic record

VenueJMIR Research Protocols · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsnot available
FundersPennsylvania Department of Public WelfareU.S. Department of Agriculture
KeywordsNutrition EducationSupplemental Nutrition Assistance ProgramPopulationMedicineGerontologyMedical educationOverweightPsychologyFamily medicineBody mass indexEnvironmental healthFood securityAgriculture

Abstract

fetched live from OpenAlex

BACKGROUND: Recent legislation established a requirement for nutrition education in federal assistance programs to be evidence-based. Recruitment of low-income persons to participate and evaluate nutrition education activities can be challenging and costly. Facebook has been shown to be a cost-effective strategy to recruit this target audience to a nutrition program. OBJECTIVE: The purpose of our study was to examine Facebook as a strategy to recruit participants, especially Supplemental Nutrition Assistance Program Education (SNAP-Ed) eligible persons, to view and evaluate an online nutrition education program intended to be offered as having some evidence base for SNAP-Ed programming. METHODS: English-speaking, low-income Pennsylvania residents, 18-55 years with key profile words (eg, Supplemental Nutrition Assistance Program, Food bank), responded to a Facebook ad inviting participation in either Eating Together as a Family is Worth It (WI) or Everyone Needs Folic Acid (FA). Participants completed an online survey on food-related behaviors, viewed a nutrition education program, and completed a program evaluation. Facebook set-up functions considered were costing action, daily spending cap, and population reach. RESULTS: Respondents for both WI and FA evaluations were similar; the majority were white, <40 years, overweight or obese body mass index, and not eating competent. A total of 807 Facebook users clicked on the WI ad with 73 unique site visitors and 47 of them completing the program evaluation (ie, 47/807, 5.8% of clickers and 47/73, 64% of site visitors completed the evaluation). Cost per completed evaluation was US $25.48; cost per low-income completer was US $39.92. Results were similar for the FA evaluation; 795 Facebook users clicked on the ad with 110 unique site visitors, and 73 completing the evaluation (ie, 73/795, 9.2% of ad clickers and 73/110, 66% of site visitors completed the evaluation). Cost per valid completed survey with program evaluation was US $18.88; cost per low-income completer was US $27.53. CONCLUSIONS: With Facebook we successfully recruited low-income Pennsylvanians to online nutrition program evaluations. Benefits using Facebook as a recruitment strategy included real-time recruitment management with lower costs and more efficiency compared to previous data from traditional research recruitment strategies reported in the literature. Limitations prompted by repeated survey attempts need to be addressed to optimize this recruitment strategy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Protocol
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Protocol
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.170
metaresearch head score (Gemma)0.184
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.170
Threshold uncertainty score0.901

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1700.184
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0070.003
Science and technology studies0.0060.003
Scholarly communication0.0050.005
Open science0.0060.008
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0690.029

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.703
GPT teacher head0.703
Teacher spread0.001 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Other design
Domainnot available
GenreProtocol

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations57
Published2013
Admission routes1
Has abstractyes

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