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Record W2115175568 · doi:10.7189/jogh.04.020410

Demand generation and social mobilisation for integrated community case management (iCCM) and child health: Lessons learned from successful programmes in Niger and Mozambique

2014· article· en· W2115175568 on OpenAlexfundno aff
Alyssa Sharkey, Sandrine Martin, Teresa Cerveau, Erica Wetzler, Rocio Berzal

Bibliographic record

VenueJournal of Global Health · 2014
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
FundersEuropean CommissionGovernment of CanadaUNICEF
KeywordsFocus groupStakeholderEmpowermentEconomic growthBehavior change communicationStakeholder engagementPolitical sciencePublic relationsMedicineBusinessEnvironmental healthHealth servicesEconomicsPopulation

Abstract

fetched live from OpenAlex

AIM: We present the approaches used in and outcomes resulting from integrated community case management (iCCM) programmes in Niger and Mozambique with a strong focus on demand generation and social mobilisation. METHODS: We use a case study approach to describe the programme and contextual elements of the Niger and Mozambique programmes. RESULTS: Awareness and utilisation of iCCM services and key family practices increased following the implementation of the Niger and Mozambique iCCM and child survival programmes, as did care-seeking within 24 hours and care-seeking from appropriate, trained providers in Mozambique. These approaches incorporated interpersonal communication activities and community empowerment/participation for collective change, partnerships and networks among key stakeholder groups within communities, media campaigns and advocacy efforts with local and national leaders. CONCLUSIONS: iCCM programmes that train and equip community health workers and successfully engage and empower community members to adopt new behaviours, have appropriate expectations and to trust community health workers' ability to assess and treat illnesses can lead to improved care-seeking and utilisation, and community ownership for iCCM.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.053
GPT teacher head0.375
Teacher spread0.322 · 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.

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

Citations18
Published2014
Admission routes1
Has abstractyes

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