Socio-cultural aspects of oral health among the Fulani in Ferlo (Senegal): A qualitative study
Bibliographic record
Abstract
OBJECTIVES: Health is a subjective concept that considers the social, cultural, environmental and behavioural problems of the individual. This study was conducted with the objective of better understanding the sociocultural aspects related to the oral health of the Fulani populations of Ferlo, Senegal. METHODS: The study was qualitative and based in the area of the Great Green Wall (GGW) in the region known as Ferlo, northern Senegal. Data were collected by semi-structured interviews in a sample of the population and through discussions with a focus group. It concerned health and aesthetics of the teeth, care and traditional herbal recipes, teeth and superstitions. RESULTS: It appears that people were using the toothpick, the chewing stick and/or charcoal to clean their teeth. Confusion persisted with respect to the types of food consumed that were implicated in the occurrence of dental caries: tea, rice, 'jumbo' (a seasoning spice) and tobacco. 'Borom bop', which means 'master of the head', was the most commonly reported cause of caries. Healthy, beautiful teeth were attributes of beauty and elegance, enhanced by tattoos and crafted crowns in the Fulani. Their health problems were generally managed by healers or traditional practitioners who based their practices on empirical and 'handed down' knowledge. Socio-anthropological meanings were given to children with neonatal teeth. CONCLUSIONS: It is therefore important to consider the sociocultural aspects in oral health projects and programmes; the place of herbal medicine in dentistry should be recognized and maybe researched in the region of the Great Green Wall.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".