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Record W2323942472 · doi:10.1158/1940-6207.prev-11-b20

Abstract B20: Access to dental service in an urban low-income community and its impact on oral cancer prevention

2011· article· en· W2323942472 on OpenAlexaffabout
Keith Hau, Samson Ng, Yiping Liu, Christopher Zed, Doreen Littlejohn, Catherine F. Poh

Bibliographic record

VenueCancer Prevention Research · 2011
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineFamily medicineCancerService (business)IncentiveDental insuranceEnvironmental healthGerontologyOral healthBusiness

Abstract

fetched live from OpenAlex

Abstract Introduction: Vancouver's Downtown Eastside (DTES) community, one of poorest locales in Canada, has shown to be high-risk for oral mucosal abnormalities, including oral cancer. From our previous study, this community not only exposes to high-risk factors but has also high incidence of oral cancer: one oral cancer patient identified in 150 screened dental patients. This is much higher comparing to the rest of Canada and the US (1:10,000). Studies have shown that regular dental care is related to early-staged oral cancer detection. Traditionally dentistry is within the private health sector and access to dental service can be challenging to the DTES residents. Objective: 1) To determine the frequency of oral cancer risk behaviors; 2) To explore the available social assistant program to dental care; 3) To assess the dental care service utilization and its potential barriers in the DTES. Methods: To increase access, mobile screening clinics are set up at 3 main gathering locations: Vancouver Native Health Society (VNHS), Women's centre (WC) and LifeSkill's Centre (LC). Eligibility includes those of age 18 or over, reside in DTES at least for the past 3 months, and are able to sign a consent to participate. Using person-to-person interview, questionnaires for demographics, risk behaviors, perceived dental health status, available health care assistance programs, and dental care service utilization are used collect data. The oral health status is obtained through dental and oral mucosal examination by a dentist and an oral pathologist respectively. Each participant is given an incentive package as well as a five dollar honorarium after completion of questionnaire and screening. Results: A total of 106 participants were screened through 3 mobile clinics at each centre (VNHS: 40; WC: 44; LS: 22). Participants are more female (57%), middle-aged (average age, 48.3 years), Aboriginal (65%), low-income (annual income ≤12000, 71%), and less educated (≤ grade 12, 57%). Many are heavy smokers (20 or more pack-years, 32%), heavy drinkers (male ≥ 3 units or female ≥ 2 units daily, 57%), and have high-risk behavior vulnerable to the human papillomavirus infection (≥ 6 oral sex partners, 27%). Their host immunity is compromised by the prevalence of HIV (41%) and Hepatitis C viral (42%) infection. One in two participants has never heard of oral cancer. Among 106 screened, 20% show oral mucosal abnormalities, including infection (N=11), trauma (N = 7), and potential premalignant conditions requiring further investigation (N=3). The majority (71%) is under coverage of limited assistance from Federal (51%) and/or Provincial government (20%) but only half of the participants (49%) used any dental services within the past 12 months. This is much less comparing to the national level (74%). For those having social assistance in dental care, the main barriers are cost or ‘not enough insurance coverage’ (28%), and not the priority (9%). Conclusion: The pilot data show that this is a community at high-risk for oral cancer. Regular screening is essential. How to use existing limited resources and raising awareness of oral cancer are vital for oral cancer prevention in this high-risk community. Citation Information: Cancer Prev Res 2011;4(10 Suppl):B20.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.229
GPT teacher head0.539
Teacher spread0.309 · 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.

Study designObservational
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
Published2011
Admission routes2
Has abstractyes

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