Evidence map›Paper›PMID 31919766›Full record

ArticleThe journal of primary prevention2020

Minimizing Health-Compromising Behaviors via School-Based Programs: An Optimization Approach.

Niloofar Bavarian, Banafsheh Behzad, Sheena Cruz

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Article in The journal of primary prevention, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers 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

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

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

3 authors.

Niloofar BavarianCollege of Health and Human Services, California State University, Long Beach, 1250 Bellflower Blvd, Long Beach, CA, 90840, USA. Niloofar.Bavarian@csulb.edu.
Banafsheh BehzadCollege of Business Administration, California State University, Long Beach, Long Beach, CA, USA.
Sheena CruzCollege of Health and Human Services, California State University, Long Beach, 1250 Bellflower Blvd, Long Beach, CA, 90840, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

School health programs are united by their desire to promote health and health-related outcomes among youth. They are also united by the fact that their expected effects are contingent on successful program implementation, which is often impeded by a multitude of real-world barriers. Techniques used in management science may help optimize school-based programs by accounting for implementation barriers. In this exploratory study, we present a detailed example of the first known application of linear programming (LP), which is an optimization technique, to Positive Action (PA). PA is a social emotional and character development program that includes a six-unit, teacher-delivered, classroom curriculum. We specify how we used LP to calculate the optimal levels of program implementation needed to minimize substance use, subject to known levels of implementation barriers (e.g., disruptive behavior, teacher education, teacher attitudes towards character development, school resources, and school safety). We found that LP is a technique that can be applied to data from a school health program. Specifically, we were able to develop a model that calculated the number of lessons that should be taught to minimize a specific health-compromising behavior, given expected levels of predetermined implementation barriers. Our findings from this exploratory study support the utility of applying LP during the program planning and implementation processes of school health programs.

Indexed as

Health BehaviorResource AllocationSchool Health ServicesChicagoCurriculumHealth PromotionHumansModels, StatisticalRisk Reduction BehaviorSubstance-Related DisordersCharacter educationLinear programmingProgram planningSchool programs

Identifiers

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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.