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Record W1597231605 · doi:10.1300/j081v19n03_10

The Effect of Viewing a Landscape on Physiological Health of Elderly Women

2006· article· en· W1597231605 on OpenAlexaff
Joyce Tang, Robert D. Brown

Bibliographic record

VenueJournal of Housing for the Elderly · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsUniversity of GuelphCalgary Laboratory Services
Fundersnot available
KeywordsBlood pressureHeart rateNatural (archaeology)MedicineAffect (linguistics)Elderly peopleCardiologyGerontologyGeographyPsychologyInternal medicineCommunication

Abstract

fetched live from OpenAlex

Abstract A quasi-experiment was undertaken to measure physiological characteristics of elderly women as they viewed different landscapes. Blood pressure and heart rate were monitored as elderly women living in a retirement centre viewed a natural landscape, a built landscape, and a control room with no view to the outside. Other characteristics of the individuals and the settings that have been shown to affect blood pressure and heart rate were controlled. The results indicated that, in all cases, viewing the natural landscape resulted in lower systolic and diastolic blood pressures and lower heart rates than those measured in the control room. Viewing the built landscape also had the general effect of lowering blood pressures and heart rates, but the effect was less consistent and the magnitude was smaller than that caused by the natural landscape. Lowering of blood pressure and heart rate have both been shown to be positively correlated with increased health and well-being, indicating the benefit of simply viewing a landscape. These results have important implications for design of housing for the elderly. Even if individuals are unable or unwilling to go outside, they can still benefit from seeing out into a landscape.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.231

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.015
GPT teacher head0.268
Teacher spread0.253 · 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

Citations43
Published2006
Admission routes1
Has abstractyes

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