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Record W4367016773 · doi:10.7202/1098699ar

La transdisciplinarité dans une recherche en santé publique en Tunisie

2023· article· fr· W4367016773 on OpenAlexaffvenue
Aïcha Boukthir, François Guillemette

Bibliographic record

VenueEnjeux et société Approches transdisciplinaires · 2023
Typearticle
Languagefr
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Les problèmes complexes de la santé publique nécessitent des stratégies de recherche intégratrices qui transcendent le cadre inter et multidisciplinaire. Ces stratégies s’appuient sur l’approche transdisciplinaire parce que celle-ci permet, d’une part, le dialogue entre les sciences de la santé et les sciences humaines et, d’autre part, de comprendre les phénomènes tout en engageant les parties prenantes dans des voies de solutions. Le but ultime est une co-construction des savoirs, à partir des savoirs académiques et des savoirs populaires, associant différentes disciplines et différents types d’acteurs – thérapeutes, patients, institutions de soins – pour répondre aux défis spécifiques complexes de la santé publique. Dans cet article, nous aborderons les aspects épistémologiques et méthodologiques de l’approche transdisciplinaire en visant à mettre en lumière sa pertinence à travers une expérience de recherche sur le contrôle de la leishmaniose cutanée zoonotique (LCZ). Cette expérience vécue au service d’épidémiologie médicale à l’Institut Pasteur de Tunis concerne un problème de santé publique complexe en Tunisie et ailleurs dans le monde.

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)0.017
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.005
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0010.001

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 head0.090
GPT teacher head0.431
Teacher spread0.341 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2023
Admission routes2
Has abstractyes

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