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Record W4385813892 · doi:10.1136/bmjgh-2023-012349

Kansa talk: mapping cancer terminologies in Bagamoyo, Tanzania towards dignity-based practice

2023· article· en· W4385813892 on OpenAlexfundno aff
Mohamed Yunus Rafiq, Daniel W Krugman, Fatima Bapumia, Zachary Obinna Enumah, Hannah Wheatley, Kheri Tungaraza, René Gerrets, Steve Mfuko, Brian J. Hall, Optatus Kasogela, Athumani Litunu, Peter J. Winch

Bibliographic record

VenueBMJ Global Health · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Security and Public Health
Canadian institutionsnot available
FundersYork UniversityWellcome TrustNew York University Shanghai
KeywordsPublic healthDignitySociologyPublic relationsQualitative researchTanzaniaMedicinePolitical scienceNursingSocial scienceSocioeconomics

Abstract

fetched live from OpenAlex

This paper reports and examines the results of qualitative research on the use of local cancer terminology in urban Bagamoyo, Tanzania. Following recent calls to unify evidence and dignity-based practices in global health, this research locates local medical sociolinguistics as a key place of entry into creating epistemologically autonomous public health practices. We used semistructured ethnographic interviews to reveal both the contextual and broader patterns related to use of local cancer terminologies among residents of Dunda Ward in urban Bagamoyo. Our findings suggest that people in Bagamoyo employ diverse terms to describe and make meanings about cancer that do not neatly fit with biomedical paradigms. This research not only opens further investigation about how ordinary people speak and make sense of the emerging cancer epidemic in places like Tanzania, but also is a window into otherwise conceptualisations of 'intervention' onto people in formerly colonised regions to improve a health situation. We argue that adapting biomedical concepts into local sociolinguistic and knowledge structures is an essential task in creating dignity-based, evidence-informed practices in global health.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.761
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.083
GPT teacher head0.471
Teacher spread0.388 · 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 designNot applicable
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

Citations2
Published2023
Admission routes1
Has abstractyes

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