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Record W2952733526 · doi:10.82308/21096

Oral health beliefs and dental health care-seeking behaviors among Chinese immigrants

2006· article· en· W2952733526 on OpenAlexfundaboutno aff
Mei Dong

Bibliographic record

VenueeScholarship@McGill (McGill) · 2006
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchMcGill University
KeywordsImmigrationEthnic groupThematic analysisMedicineCultural competenceCultural diversityHealth careChinese americansQualitative researchNursingFamily medicinePsychologySociologyPolitical sciencePedagogy

Abstract

fetched live from OpenAlex

Understanding culturally related health values and identifying ethnically specific health seeking pathways can help health care providers supply culturally competent services and enhance cooperation with patients of different backgrounds. Cultural competency training, notably through cultural awareness courses, promotes understanding of the impact of social factors on illness and thus prepares medical and dental students to better serve their patients. Cultural awareness can also help preventive health programs fit community needs and cultural contexts. Despite the fact that Chinese immigrants are the fastest growing ethnic minority in North America, few studies have been published on their beliefs and health-seeking behaviours following immigration. We thus lack information on how Chinese immigrants regard dental health and manage their dental problems. Objective. The aims of this study were to explore how oral illness is viewed by Chinese immigrants in Montreal, Canada and how they manage dental problems. Methods. We conducted a qualitative research study based on semi-structured, one-on-one interviews and thematic analyses of the transcribed interviews. Twelve adult Montreal Chinese immigrants with a high level of education participated in the study. Results. Chinese immigrants in Montreal have a good understanding of dental caries in terms of its etiology, process, and ways to prevent and treat it. It thus seems that there is no major cultural barrier between this type of immigrant and oral health care professionals in regard to dental caries. However, we also observed that traditional beliefs and medications coexist with scientific dental knowledge and professional treatments concerning problems such as gingival swelling, gingival bleeding, and bad breath. In the case of gingival swelling, for instance, participants identified etiological factors that referred to both cultures: local factors referred to oral hygiene and were related to scientific culture, whereas general factors referred to traditional knowledge ("internal fire"). Chinese immigrants' dental health seeking pathways include self-treatment, consulting a dentist in Canada or in China during a return visit, and obtaining Chinese traditional medicine. The dental health seeking pathways varied depending on the circumstances. For dental caries and other acute diseases such as toothache, Chinese immigrants prefer to consult a dentist. For chronic diseases, some of them rely on self-treatment or an alter-native treatment such as traditional Chinese medicine. The language barrier, financial problems and lack of trust are the main factors affecting Chinese immigrants' access to dental care services in Canada. Former bad medical or dental experience among Chinese immigrants causes a loss of trust in Western medicine and dentistry and influences the decision to seek alternative treatments. Conclusion. This study suggests that, in order to facilitate dentist-patient communication; oral health professionals should be informed of immigrants' representation of oral health and illness, and that Chinese immigrants should be provided with basic scientific knowledge.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.566
Threshold uncertainty score0.872

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.365
Teacher spread0.338 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2006
Admission routes2
Has abstractyes

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