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Record W4205309713 · doi:10.1063/5.0069768

The effects of public library architecture on users’ mental health

2021· article· en· W4205309713 on OpenAlexaff
Shahnaz Javdani, Mohammadreza Vasfi, Nader Naghshineh, Parastoo Karami, Azadeh Mehrpouyan

Bibliographic record

VenueAIP conference proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsBritish Columbia Institute of Technology
Fundersnot available
KeywordsArchitectureComputer scienceMental healthWorld Wide WebPsychologyPsychiatryArtVisual arts

Abstract

fetched live from OpenAlex

This article analyzes the architectural impact of public library areas on the tegmental health of users. The study also presents adequate criteria and models to identify environmental incentives in the living space and their effects on people and to identify and evaluate environmental indicators. The study developed a library checklist with standards, requirements, and trends for construction and outdoor spaces in public libraries. The research is carried out in all relevant public libraries of the Iranian Institute for Public Libraries in Tehran. The analysis was performed using the descriptive survey method and two types of questionnaires were used, including the general health questionnaire (GHQ) and the investigators' questionnaire. The results show that the dependent variable (the architectural scope of the public library) can influence the independent variable (the mental health of the users). This study confirms that there is a significant correlation between the architectural elements of the public library and the mental health of the users.

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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.029
GPT teacher head0.284
Teacher spread0.255 · 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
Published2021
Admission routes1
Has abstractyes

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