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Behaviour Change Communication Using Mobile Phones: Implications for Infant and Young Child Feeding Interventions

2017· article· en· W2942354883 on OpenAlexaffabout
Alison Mildon, Daniel Sellen

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsmHealthBreastfeedingPsychological interventionMobile phoneMedicineBreast feedingLeverage (statistics)Behavior change communicationRelevance (law)Grey literatureNursingEnvironmental healthMEDLINEComputer sciencePopulationPediatricsHealth servicesTelecommunicationsPolitical science

Abstract

fetched live from OpenAlex

Background Improving infant and young child feeding (IYCF) practices is critical to reducing the burden of preventable malnutrition, morbidity and mortality in low‐ and middle‐income countries (LMIC). Breastfeeding and complementary feeding practices can be improved through contextualized behaviour change communication (BCC), but access is limited. The proliferation of mobile phones in LMIC offers new opportunities for BCC delivery, with potential for large scale implementation. There is need for delivery science to determine how best to leverage mobile phones to support IYCF, and how to optimize the relevance and quality of such “mHealth” innovations. Objectives A scoping review was designed to identify and map current knowledge on IYCF BCC delivery using mobile phones, and to identify priorities for further research and programming. Methods Following the methodology proposed by Arksey & O'Malley (2005), we identified, extracted and synthesized data from searches of the published and grey literature, and consultation with mHealth researchers and implementers. We selected for analysis published articles and programmatic documents reporting on the feasibility, effectiveness or implementation lessons learned related to the use of mobile phones for BCC addressing maternal, newborn and child health (MNCH) practices in LMIC. We summarized, assessed and classified current evidence according to the main BCC delivery approach and the application of seven BCC techniques, based on the categories proposed by Briscoe & Aboud (2012). Results Seventeen published studies and nineteen grey literature reports met inclusion criteria, including eight effectiveness studies with at least one IYCF indicator. Four mobile phone‐based BCC delivery approaches were identified: direct messaging, job aid applications, voice counseling and interactive media. These approaches differ markedly in intervention intensity and utilization of interpersonal communication and problem solving support, two BCC techniques critical for improving IYCF. Individual trials of interventions using direct messaging, voice counseling or a combination have shown positive effects on exclusive breastfeeding. Mhealth approaches to deliver BCC addressing multiple MNCH topics have shown limited effects on IYCF practices, but the evidence base is small. Conclusions The four BCC delivery approaches can support interpersonal communication for improved IYCF practices in LMIC, but vary considerably in implementation elements. Both content development and selection of delivery approaches should be contextualized, guided by formative research. Interventions that explicitly target specific IYCF practices and emphasize personalized problem solving support are likely to be most effective. Further research on effectiveness, implementation pathways and cost‐effectiveness of specific BCC delivery approaches is needed. Support or Funding Information This work was carried out with the aid of a grant from the Micronutrient Initiative, Ottawa, Canada through the financial assistance of the Government of Canada through the Department of Foreign Affairs, Trade and Development Canada

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.121
GPT teacher head0.387
Teacher spread0.266 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations3
Published2017
Admission routes2
Has abstractyes

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