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Record W4405063148 · doi:10.9745/ghsp-d-23-00509

Understanding Integrated Community Case Management Institutionalization Processes Within National Health Systems in Malawi, Mali, and Rwanda: A Qualitative Study

2024· article· en· W4405063148 on OpenAlexaff
Alyssa L. Davis, Erica Felker-Kantor, Jehan Ahmed, Zachariah Jezman, Beh Kamaté, John Munthali, Noella Umulisa, Oumar Yattara

Bibliographic record

VenueGlobal Health Science and Practice · 2024
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsImpact
FundersPresident's Malaria InitiativeUnited States Agency for International Development
KeywordsInstitutionalisationQualitative researchImplementation researchContext (archaeology)Economic growthMedicinePolitical sciencePsychological interventionPublic relationsNursingSociologySocial scienceGeographyPsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: Since 2012, the World Health Organization (WHO) and UNICEF have recommended integrated Community Case Management (iCCM) of childhood illnesses as an intervention delivered by community health workers (CHWs) in areas with limited access to health facilities to increase access to lifesaving interventions for children younger than 5 years with malaria, pneumonia, or diarrhea. In recent years, the importance of institutionalizing iCCM and community health more broadly within national health systems has become increasingly recognized. METHODS: This qualitative study sought to identify and describe processes of iCCM institutionalization from the perspectives of health system actors. A total of 51 semistructured interviews were conducted with purposefully selected key informants in 3 countries: Malawi, Mali, and Rwanda. Thematic analysis of coded interview data was conducted, and country documentation was reviewed to provide contextual background for qualitative interpretation. The study was informed by a newly developed iCCM Institutionalization Framework, which conceptualizes the process of institutionalization through a maturity model of phases (i.e., awareness, experimentation, expansion, consolidation, and maturity) with 4 drivers: core values, leadership, resources, and policy. RESULTS: According to key informant narrative descriptions, processes of iCCM institutionalization reflected a progression of maturity phases, which were iterative rather than linear in progression. All 4 drivers of institutionalization as conceptualized within the iCCM Institutionalization Framework were described by key informants as contributing to the advancement of iCCM institutionalization within their countries. Key informants emphasized the need to continually strengthen or reinforce iCCM institutionalization for it to be sustained within the context of wider health system dynamics. CONCLUSION: Overall, key informants viewed government ownership and integration within national systems to define the status of iCCM institutionalization. Further development of the iCCM Institutionalization Framework and other practical sensemaking models could assist health system actors in advancing institutionalization of iCCM and other health interventions.

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)0.023
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.027
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0100.012
Scholarly communication0.0050.007
Open science0.0020.007
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.212
GPT teacher head0.503
Teacher spread0.291 · 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 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

Citations5
Published2024
Admission routes1
Has abstractyes

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