MétaCan
Menu
Back to cohort
Record W6991027409

Examining Self-Rated Oral Health and Self-Rated Oral Need among Adults Aged 55+ in Ontario

2022· other· en· W6991027409 on OpenAlexaboutno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2022
Typeother
Languageen
FieldHealth Professions
TopicTherapeutic Uses of Natural Elements
Canadian institutionsnot available
Fundersnot available
KeywordsOral healthBiopsychosocial modelPublic healthAttritionCross-sectional studyNational Health Interview SurveyAffect (linguistics)MEDLINETooth loss
DOInot available

Abstract

fetched live from OpenAlex

There is a paucity of research on self-rated oral health (SROH) and self-rated oral needs (SRON) within aging populations. As such, the research objectives of this study were to assess factors associated with SROH and assess how SRON indicators correlate with SROH among Ontarian adults aged 55 years and greater. Data from the 2017-2018 Annual Component of the Canadian Community Health Survey were used. Linear regression estimated the associations between a range of biopsychosocial factors and SROH. Pearsons correlation estimated associations between SRON and SROH. Smoking, poor general health rating, and never visiting the dentist were associated with an increased likelihood of poorer SROH. Satisfaction with teeth/denture appearance was correlated with better SROH. This study is first to consider both SROH and SRON in Ontarian older adults. More research is needed to inform applied practice and policy that aim to improve services and promote oral health with aging.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.037
GPT teacher head0.244
Teacher spread0.207 · 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 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueYork University Digital Library (York University)Same topicTherapeutic Uses of Natural ElementsFrench-language works237,207