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Assessing the oral health of an ageing population: methods, challenges and predictors of survey participation

2011· article· en· W1634566841 on OpenAlex
Debora Matthews, Martha Brillant, Joanne Clovis, Mary McNally, Mark Filiaggi, Robert D. Kotzer, Herenia P. Lawrence

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueGerodontology · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicSurvey Methodology and Nonresponse
Canadian institutionsUniversity of TorontoDalhousie University
FundersInstitute of Musculoskeletal Health and ArthritisCanadian Institutes of Health ResearchHealth CanadaNova Scotia Health Research Foundation
KeywordsMedicineLogistic regressionTelephone interviewPopulationGerontologyCross-sectional studyTelephone surveyObservational studyFamily medicineOral examinationDemographyOral healthEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVES: To examine predictors of participation and to describe the methodological considerations of conducting a two-stage population-based oral health survey. METHODS: An observational, cross-sectional survey (telephone interview and clinical oral examination) of community-dwelling adults aged 45-64 and ≥65 living in Nova Scotia, Canada was conducted. RESULTS: The survey response rate was 21% for the interview and 13.5% for the examination. A total of 1141 participants completed one or both components of the survey. Both age groups had higher levels of education than the target population; the age 45-64 sample also had a higher proportion of females and lower levels of employment than the target population. Completers (participants who completed interview and examination) were compared with partial completers (who completed only the interview), and stepwise logistic regression was performed to examine predictors of completion. Identified predictors were as follows: not working, post-secondary education and frequent dental visits. CONCLUSION: Recruitment, communications and logistics present challenges in conducting a province-wide survey. Identification of employment, education and dental visit frequency as predictors of survey participation provide insight into possible non-response bias and suggest potential for underestimation of oral disease prevalence in this and similar surveys. This potential must be considered in analysis and in future recruitment strategies.

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.

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)0.057
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0570.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.696
GPT teacher head0.570
Teacher spread0.125 · 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