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Record W2188227174 · doi:10.5539/enrr.v5n4p63

Gender Variations in Wellbeing Indicators between Urban and Mountain Landscape Environments

2015· article· en· W2188227174 on OpenAlexvenueno aff
Henry Ojobo, Sapura Mohamad, Ismail Said

Bibliographic record

VenueEnvironment and Natural Resources Research · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsnot available
Fundersnot available
KeywordsPulse rateBlood pressureRespiratory rateAltitude (triangle)DemographyGeographyEcologyMedicineHeart rateBiologySociologyInternal medicine

Abstract

fetched live from OpenAlex

<p class="1Body">The issue of variations in physiological indicators of wellbeing based on gender serves as incentive for natural landscape environment interactions. This study examined gender variations in blood pressure, pulse rate and respiratory rate between contact with low-altitude urban (pretest) and mountain landscape environments (posttest). To attain the goal of this study, 38 respondents (16 males, 22 females) participated in the seven-day experimental study. Pretest and posttest measures of blood pressure, pulse rate and respiratory rate were elicited from both male and female respondents at the urban environment within the first three days and at the mountain landscape environment the following three days. Results show that both male and female systolic blood pressure increased at the mountain landscape environment while their diastolic blood pressure reduced marginally. There was no difference in gender response in terms of pulse rate. Conversely, male respondents experienced reduction of respiratory rate at the mountain landscape environment while female respondents experienced increase. Findings suggest that the only apparent difference in gender response is in their respiratory rate. The extent to which gender might be related to physiological wellbeing through contact with natural mountain landscape environment is revealed. Hence, a platform is set for policy makers and governments for the creative harnessing of mountain landscape environments.</p>

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.607

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 science0.0000.001
Research integrity0.0000.001
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 head0.041
GPT teacher head0.304
Teacher spread0.263 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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