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Record W2947101436 · doi:10.3390/ijerph16111952

Oral Health Beliefs, Attitudes, and Practices of South Asian Migrants: A Systematic Review

2019· review· en· W2947101436 on OpenAlexaboutno aff
Mehak Batra, Sabrina Gupta, Bircan Erbas

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2019
Typereview
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental healthPsychologyGeographyMedicine

Abstract

fetched live from OpenAlex

Oral health is a burden among all populations and is linked with major chronic diseases such as cardiovascular diseases. Migrants, in particular South Asians, have poor oral health which requires further understanding to better inform oral health interventions by targeting specific aspects of this heterogenous South Asian population. This review is undertaken to systematically synthesize the evidence of oral health understandings, knowledge, attitudes, beliefs, practices, and behaviors of South Asian migrants residing in high-income countries. A comprehensive systematic search of seven electronic databases and hand-searching for peer-reviewed studies was conducted. All study designs were included, and quality assessment conducted. Of the 1614 records identified, 17 were included for synthesis and 12 were quantitative in design. These studies were primarily conducted in the UK, USA, Canada, and Europe. South Asian migrants had inadequate oral health knowledge, attitudes, and practices-influenced by culture, social norms, and religiosity. In the absence of symptoms, preventive oral hygiene practices were limited. Barriers to access varied with country of origin; from lack of trust in dentists and treatment cost in studies with India as the country of origin, to religiosity, among poorer nations such as Bangladesh. Fewer studies focused on recent arrivals from Bhutan or the Maldives. Culturally and socially appropriate strategies must be developed to target oral health issues and a "one-size" fits all approach will be ineffective in addressing the needs of South Asian migrants.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.429
Threshold uncertainty score0.903

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.201
GPT teacher head0.517
Teacher spread0.316 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations56
Published2019
Admission routes1
Has abstractyes

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