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Record W3165844022 · doi:10.1136/medhum-2020-012130

A black dog enters the home: hunger and malnutrition in Malawi

2021· article· en· W3165844022 on OpenAlexafffund
Anne Dressel, Elizabeth Mkandawire, Lucy Mkandawire‐Valhmu, Elizabeth Dyke, Clement Bisai, Hazel Kantayeni, Peninnah Kako, Brittany Ochoa-Nordstrum

Bibliographic record

VenueMedical Humanities · 2021
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsEmployment and Social Development Canada
FundersGlobal Affairs CanadaGovernment of Canada
KeywordsMalnutritionFocus groupIndigenousContext (archaeology)EmpowermentQualitative researchIntervention (counseling)PovertySocioeconomicsPsychologySociologyGender studiesMedicinePolitical scienceNursingGeographySocial science

Abstract

fetched live from OpenAlex

Hunger and inadequate nutrition are ongoing concerns in rural Malawi and are exemplified in traditional proverbs. Traditional proverbs and common expressions offer insight into commonly held truths across societies throughout sub-Saharan Africa. Strong oral traditions allow community beliefs embodied in proverbs to be passed down from generation to generation. In our qualitative study, we conducted 8 individual and 12 focus group interviews with a total of 83 participants across two districts in rural central Malawi with the aim of soliciting context-specific details on men and women's knowledge, attitudes and practices related to nutrition, gender equality and women's empowerment. Each interview began by asking participants to share common proverbs related to nutrition. Our qualitative analysis, informed by an indigenous-based theoretical framework that recognises and centres African indigenous knowledge production, yielded six themes: 'a black dog enters the home', 'don't stay with your hands hanging', 'a man is at the stomach', 'showers have fallen', 'we lack peace in our hearts' and 'the hunger season'. Traditional proverbs can provide insight into the underlying causes of hunger and malnutrition. Physicians, nurses and other allied health professionals around the world have a role to play in addressing hunger and malnutrition, which have been exacerbated by climate change. We have an ethical duty to educate ourselves and others, and change our behaviours, to mitigate the root causes of climate change, which are contributing to food insecurity and resultant poor health outcomes in countries like Malawi.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.158
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.022
GPT teacher head0.268
Teacher spread0.246 · 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
Published2021
Admission routes2
Has abstractyes

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