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Record W2246380569 · doi:10.1017/s1041610202008153

Measurement of the Influence of the Physical Environment on Adverse Health Outcomes: Technical Report From the Canadian Study of Health and Aging

2001· article· en· W2246380569 on OpenAlexaffabout
Carolyn Wentzel, Heather Rose, Kenneth Rockwood

Bibliographic record

VenueInternational Psychogeriatrics · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsInstitutionalisationDementiaGerontologyPhysical healthAging in placePsychologyMedicineEnvironmental healthMental healthPsychiatry

Abstract

fetched live from OpenAlex

A paucity of information exists to characterize the relationship between the health status of elderly people and their physical environment. The Canadian Study of Health and Aging (CSHA) is a multicenter study of the distribution of dementia among community-dwelling and institutionalized Canadians aged 65 years and older. The study also provides the opportunity to examine issues such as the physical environment which may be related to the health of elderly people. Six items were used to assess the cleanliness, neatness, and maintenance of the inside and outside of the homes of 8,134 community-dwelling individuals. Data were also obtained to evaluate cognition, physical health, and functional capacity. Five years after the original survey, information pertaining to subsequent institutionalization and/or mortality was obtained. A significant relationship was found between classification of physical environment and the outcomes of institutionalization and mortality. The likelihood of both adverse outcomes was notably higher for individuals living in a "less than ideally maintained environment" compared to an "ideally maintained environment." Limitations of the six items used to assess the physical environment and ways in which to improve the sensitivity of the items, consequently avoiding measurement bias, are discussed.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.031
GPT teacher head0.335
Teacher spread0.304 · 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 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

Citations6
Published2001
Admission routes2
Has abstractyes

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