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Seniors' attitudes: oral health and quality of life

2004· review· en· W2086893478 on OpenAlexaffabout
Audrey Penner, Vianne Timmons

Bibliographic record

VenueInternational Journal of Dental Hygiene · 2004
Typereview
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsMedicineOral healthQuality of life (healthcare)Family medicineQuality (philosophy)Environmental healthGerontologyNursing

Abstract

fetched live from OpenAlex

The objective of this study was to determine what impact, if any, oral health was having on the quality of life for selected seniors in Prince Edward Island, Canada. The attitudes of seniors towards oral health and its relationship to quality of life is important to define. This self-reported assessment provides information on this particular relationship. The research design was a random cluster sampling that covered all geographical areas of Prince Edward Island. It represented the cultural diversity within these geographical areas. The survey instrument selected was the Subjective Oral Health Indicators' Status, a validated survey instrument. This particular instrument addressed all the issues raised in the objectives. Data were analysed using Pearson's correlation with age and number of teeth present. The independent t-test was used to identify differences in responses by gender. Results of the survey showed identification of individual indicators that were having an impact on quality of life. Gender differences in responses were identified in four of the eight subject areas. The level of worry/concern was inconclusive because of the high non-response rate to the last question. Non-response rates increased with each topic in the questionnaire. More research is needed to identify clinical needs of seniors on Prince Edward Island. Qualitative study to determine attitudes and beliefs could provide groundwork for future programme design.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.087
GPT teacher head0.454
Teacher spread0.367 · 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 designOther design
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

Citations12
Published2004
Admission routes2
Has abstractyes

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