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Record W4414199290 · doi:10.1371/journal.pone.0310288

Integrating co-design into formative research for a SBCC entry-point platform for nutrition-sensitive social protection programs in low -and middle-income country settings

2025· article· en· W4414199290 on OpenAlexfundno aff
Tahir Turk, Rina Rani Paul, Nilofer Fatimi Safdar, Nadia Shah, Syed Mahbubul Alam, Sohel Reza Choudhury, Kashif Shafique, Zaeema Ahmer, Anthony Wenndt, Hamidullah Khan Babar, Tannaza Sadaf, Moniruzzaman Bipul

Bibliographic record

VenuePLoS ONE · 2025
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsnot available
FundersGlobal Affairs CanadaEuropean CommissionDirektion für Entwicklung und ZusammenarbeitEidgenössisches Departement für Auswärtige Angelegenheiten
KeywordsFormative assessmentPsychological interventionSocial protectionUniversal designMEDLINEProgram Design LanguageProgram evaluation

Abstract

fetched live from OpenAlex

BACKGROUND: Achieving adequate nutrition for vulnerable populations is an objective of the Sustainable Development Goals. Nutrition-sensitive social protection programs, including those that promote nutrition through Social and Behaviour Change Communication (SBCC), have the potential to reduce malnutrition and provide social supports to those most in need. Country-level needs assessments can clarify key issues. When supported by co-design approaches, program formative research may provide more culturally contextualised SBCC for the improved delivery of nutrition social protection to vulnerable groups. This formative study from Pakistan and Bangladesh, integrated co-design to more fully explore program beneficiary knowledge, attitudes and perceptions toward nutrition social protection to inform the design of SBCC key messages and an entry-point platform to ensure effective message dissemination. METHODS: Qualitative formative research was conducted to support findings from a systematic review. Thirty semi-structured interviews with program stakeholders and 12 focus group discussions (134 participants) were conducted with program beneficiaries in Bangladesh and Pakistan. Co-design sessions supplemented the needs assessment protocol. A COREQ checklist ensured best practice approaches in research design, analysis and reporting. NVIVO 2023 qualitative software supported the thematic analysis. RESULTS: Four organising themes were identified: 1. Barriers to Program Engagement, 2. Opportunities for Program Improvement, 3. Knowledge Attitudes and Practices, and 4. Target Groups, Messaging and SBCC Entry-Points, with 21 sub-themes emerging under the four organising themes. Main barriers related to resource constraints and maladministration of SPPs while opportunities identified greater integration of cash transfers with nutritious food provision, increased engagement with key influencers in vulnerable communities, and identification of culturally nuanced messages with dissemination through preferred channels. Integrating co-design sessions provided greater ownership, participation and engagement by program beneficiaries and more pragmatic SBCC solutions to challenges identified. CONCLUSION: The needs assessments and integrated co-design sessions highlighted the benefits of close consultation with program beneficiaries in the design of culturally appropriate SBCC interventions to support nutrition-sensitive social protection programs. A SBCC entry-point platform was developed from participant recommendations to provide options for programmers on message designs, advocacy approaches and dissemination channels with the approaches applicable for a number of low -and middle-income countries where malnutrition is a major challenge.

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)0.248
metaresearch head score (Gemma)0.234
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.248
Threshold uncertainty score0.928

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2480.234
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0040.003
Science and technology studies0.0040.008
Scholarly communication0.0090.006
Open science0.0040.011
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0090.002

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 head0.106
GPT teacher head0.335
Teacher spread0.229 · 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.

Study designQualitative
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

Citations1
Published2025
Admission routes1
Has abstractyes

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