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Record W2944135304 · doi:10.2196/13141

Promoting Small Business Support of Youth Physical Activity in Low-Income, Minority Neighborhoods: Protocol for a Randomized Controlled Trial

2019· article· en· W2944135304 on OpenAlexvenueno aff
Richard R. Suminski, Shannon M. Robson, Jennie Turner, Eric Plautz

Bibliographic record

VenueJMIR Research Protocols · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
FundersNational Institute of Nursing ResearchNational Institutes of Health
KeywordsSmall businessRandomized controlled trialIntervention (counseling)Cluster randomised controlled trialBusinessPsychologyMarketingMedicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: An unacceptably high percentage of our nation's low-income, minority youth (age<18 years) are not regularly physically active. One reason for this could be their lack of access to quality youth physical activity opportunities (YPAOs). Our previous research found that small businesses (<500 employees), which represent over 99.64% (27.9/28.0 million businesses in United States) of all employers, are powerful resources for creating and improving YPAOs. In accordance with the socioecological model and established philanthropic principles, we developed an alpha version of an intervention (alpha-i) for increasing small businesses' involvement with YPAOs. OBJECTIVE: The aims of this proposed study are to (1) create a beta version (beta-i) of the intervention and (2) conduct a pilot study of its impact on small business support for YPAOs and YPAO utilization by the youth in low-income, minority neighborhoods. METHODS: The alpha-i will be refined using information from focus groups and surveys conducted with small business owners and managers, YPAO providers, and parents and guardians of the youths from low-income, predominantly minority neighborhoods. A cluster randomized controlled trial will then be conducted for 1 year to examine the effects of the refined intervention (beta-i) on small business support for YPAOs in 10 low-income, minority neighborhoods. The control group of neighborhoods (n=10) will be provided with a standard practice intervention. The primary outcome for aim 2 will be the percentage of small businesses not supporting YPAOs at baseline that subsequently provide support for YPAOs at follow-up. We also will consider the US dollar equivalent of all types of support (monetary, goods/services, and time) donated for YPAOs by small businesses. In addition, we will examine the impact of the increased small business support for YPAOs on YPAO utilization by the youth. RESULTS: As of May 1, 2019, all YPAOs and small businesses in the study neighborhoods have been identified, and surveys have begun with these groups. In addition, 9 focus groups were completed, and the data have been transcribed. We anticipate that manuscripts regarding these aspects of the study will be submitted in fall 2019. CONCLUSIONS: The proposed study is significant because it will provide evidence that an easily replicated approach can be used to increase small business support for YPAOs and that this support results in greater use of the YPAOs by youth. A logical next step will be to determine if YPAO changes resulting from increased small business support positively influence youth physical activity levels. CLINICALTRIAL: ClinicalTrials.gov NCT03936582; https://clinicaltrials.gov/ct2/show/NCT03936582. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/13141.

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

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)0.039
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.100
Threshold uncertainty score0.334

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.037
Meta-epidemiology (narrow)0.0070.005
Meta-epidemiology (broad)0.0140.005
Bibliometrics0.0040.005
Science and technology studies0.0060.004
Scholarly communication0.0060.005
Open science0.0050.003
Research integrity0.0100.013
Insufficient payload (model declined to judge)0.1000.014

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 head0.591
GPT teacher head0.715
Teacher spread0.123 · 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

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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