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Record W7057285614

Hypertension Awareness and Control Among Middle-aged and Older Adults in Canada: Findings from the Canadian Longitudinal Study on Aging

2023· article· en· W7057285614 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsLongitudinal studyBlood pressureCohort studyCohortLongitudinal dataHealth careControl (management)Association (psychology)
DOInot available

Abstract

fetched live from OpenAlex

Hypertension awareness and control reflect healthcare system efficacy to identify the condition and manage its burden. This thesis aimed to determine the prevalence of undetected and uncontrolled hypertension, and their association with social determinants in middle to older-aged adults. Current literature was reviewed, followed by a cross-sectional analysis of Canadian Longitudinal Study on Aging (CLSA) data. Hypertension prevalence in the cohort was 45.5%, of which 14.7% of hypertensives were undetected and 22% uncontrolled. Multivariable analysis results suggest that younger age and active employment are associated with undetected hypertension, and that older age, males, lower household income, and not graduating high school are associated with uncontrolled hypertension. Both outcomes were also strongly associated with not seeing a physician and not checking blood pressure in the last year. We recommend that future research identify and evaluate policies that are tailored to groups with high rates of undetected and uncontrolled hypertension.

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)0.003
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 score0.012
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.283
Teacher spread0.196 · 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
Published2023
Admission routes1
Has abstractyes

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