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Record W3198033183 · doi:10.1186/s12960-021-00645-5

Application of the Ultra-Poverty Graduation Model in understanding community health volunteers’ preferences for socio-economic empowerment strategies to enhance retention: a qualitative study in Kilifi, Kenya

2021· article· en· W3198033183 on OpenAlexfundno aff
Njeri Nyanja, Nelson Nyamu, Lucy Nyaga, Sophie Chabeda, Adélaïde Lusambili, Marleen Temmerman, Michaela Mantel, Anthony Ngugi

Bibliographic record

VenueHuman Resources for Health · 2021
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
FundersGlobal Affairs CanadaGovernment of CanadaAga Khan Foundation CanadaAga Khan Foundation
KeywordsHealth services researchQualitative researchPovertyHealth administrationCommunity healthPublic healthEmpowermentGraduation (instrument)Health economicsSocial policyMedicinePsychologyNursingMedical educationSociologyEconomic growthPolitical scienceSocial scienceEconomics

Abstract

fetched live from OpenAlex

BACKGROUND: A significant shortage of healthcare workforce exists globally. To achieve Universal Healthcare coverage, governments need to enhance their community-based health programmes. Community health volunteers (CHVs) are essential personnel in achieving this objective. However, their ability to earn a livelihood is compromised by the voluntary nature of their work; hence, the high attrition rates from community-based health programmes. There is an urgent need to support CHVs become economically self-reliant. We report here on the application of the Ultra-Poverty Graduation (UPG) Model to map CHVs' preferences for socio-economic empowerment strategies that could enhance their retention in a rural area in Kenya. METHODS: This study adopted an exploratory qualitative approach. Using a semi-structured questionnaire, we conducted 10 Focus Group Discussions with the CHVs and 10 Key Informant Interviews with County and Sub-county Ministry of Health and Ministry of Agriculture officials including multi-lateral stakeholders' representatives from two sub-counties in the area. Data were audio-recorded and transcribed verbatim and transcripts analysed in NVivo. Researcher triangulation supported the first round of analysis. Findings were mapped and interpreted using a theory-driven analysis based on the six-step Ultra-Poverty Graduation Model. RESULTS: We mapped the UPG Model's six steps onto the results of our analyses as follows: (1) initial asset transfer of in-kind goods like poultry or livestock, mentioned by the CHVs as a necessary step; (2) weekly stipends with consumption support to stabilise consumption; (3) hands-on training on how to care for assets, start and run a business based on the assets transferred; (4) training on and facilitation for savings and financial support to build assets and instil financial discipline; (5) healthcare provision and access and finally (6) social integration. These strategies were proposed by the CHVs to enhance economic empowerment and aligned with the UPG Model. CONCLUSION: These results provide a user-defined approach to identify and assess strategic needs of and approaches to CHVs' socio-economic empowerment using the UPG model. This model was useful in mapping the findings of our qualitative study and in enhancing our understanding on how these needs can be addressed in order to economically empower CHVs and enhance their retention in our setting.

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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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.998

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 head0.112
GPT teacher head0.432
Teacher spread0.320 · 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 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

Citations12
Published2021
Admission routes1
Has abstractyes

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