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Record W4392088773 · doi:10.1017/9781800105430.008

Bringing Psychiatric Epidemiology to a Senegalese “Living Laboratory”: Knowledge-Production and Erasure in the Interstices of Science

2022· other· en· W4392088773 on OpenAlexaboutno aff
Anne M. Lovell

Bibliographic record

Venuenot available
Typeother
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsnot available
Fundersnot available
KeywordsErasureEpidemiologyKnowledge productionSociologyMedicineComputer scienceKnowledge managementPathology

Abstract

fetched live from OpenAlex

Introduction Commenting on the slow penetration of applied sciences in the global South, late twentieth-century academics and development actors labeled Africa the “latest of the late developers.” Three decades later, epidemiologists decried sub-Saharan Africa's psychiatric “epidemiology gap,” in an era when global health institutions constantly solicit numbers on the distribution, frequency, and determinants of mental disorders to construct metrics like the Global Burden of Disease (GBD). This uneven advancement of postwar psychiatric epidemiology within sub-Saharan Africa troubles the assumed space between expectations and the conceptual-material resources and political will necessary to meet them. From the mid-1950s, a constellation of actors, including the World Health Organization (WHO), the World Federation of Mental Health, psychiatric leaders, and other experts, stressed the urgency for psychiatric services in “undeveloped” regions of the world. The landmark 1958 Bukavu conference (Belgian Congo) specifically focused on African psychiatry. At the WHO, experts debated whether to prioritize mental health statistics over services and whether an epidemiology of mental disorders was even possible, given the plethora of nosological and methodological conundrums encountered even in “developed” countries. In the early 1960s, the WHO consolidated a psychiatric epidemiology canon, disseminated through technical reports and international training sessions. During the turbulent decolonization of Africa and emergence of nationstates, psychiatric experts, ideas, and epidemiological tools regarding mental disorders circulated between the North and South through networks including the WHO, the US National Institute of Mental Health, the UK Medical Research Council and Institute of Psychiatry, McGill University’s Section on Transcultural Psychiatry, and other First World research institutes and psychiatric associations. More rarely, psychiatric experts and knowledge flowed to the global North from countries like Nigeria, India, and Taiwan, evidence that some young nation-states, including in sub-Saharan African, were already engaged in psychiatric epidemiology. This chapter analyzes the lesser-known trajectory of Senegal's first modern psychiatric epidemiology study, which wound in and out of the margins of these global flows of knowledge-production and dissemination. Directed by Henri Collomb (1913–1979), the celebrated founder of the pioneering psychiatric clinic and research group, the Ecole de Fann , in Dakar, Senegal’s capital, this study barely surfaces in the historiography of Senegal's scientific endeavors. Compared to other Fann clinic vestiges, it hardly attracted international attention. Hence, a legacy ignored: Senegal's only attempt to systematically ascertain in a population the prevalence of mental disorders with a standardized nosology.

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)0.033
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.038
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0080.040
Scholarly communication0.0170.030
Open science0.0020.013
Research integrity0.0080.016
Insufficient payload (model declined to judge)0.0100.002

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 head0.044
GPT teacher head0.327
Teacher spread0.283 · 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.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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