"Why do I have to clean teeth regularly?": perceptions and state of oral and dental health in a low-income rural community in Bangladesh
Bibliographic record
Abstract
The general perception that dentistry is expensive keeps many people away from \nseeking treatment from registered professionals and make them hostage to the \nservices of non-registered lay practitioners. In Bangladesh, no statistics on dental \nhealth problems or seeking dental healthcare is available which necessitates study for \ninformed planning of a preventive programme. BRAC Research and Evaluation \nDivision carried out a pilot survey in three unions of Gauripur upazila to document the \nknowledge and awareness and existing oral hygiene practices among the rural \npeople and also, to explore the care-giving practices of the health care practitioners - \nboth professionals and non-professionals. Both quantitative and qualitative methods \nwere used for data collection. Also clinical examination was done to record their \ncurrent state of oral cavity. Findings reveal poor oral and dental health condition of \nthe survey population and their lack of knowledge and awareness conducive to good \noral and dental health. Findings also reveal their reliance on informal sector providers \nfor treatment of oral and dental health illnesses due to non-availability of qualified \nprofessionals. Oral hygiene practice is a neglected chore in the daily routine of the \nsurvey population as revealed through real life observation in the study area. The \ncommunity people hardly used tooth brush and/or tooth paste/powder. Instead, they \nused various abrasive materials like charcoal powder, branches of trees claimed to \nhave medicinal properties, etc. for cleaning teeth which is damaging, and in turn, \ncause different oral and dental health problems. The implication of these findings for \nprogramme development is discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".