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Record W2544996729 · doi:10.5539/ass.v12n12p109

Walking Aids for Older Adults: Review of End-User Needs

2016· article· en· W2544996729 on OpenAlexvenueno aff
Nazanin Mansouri, Khaled Goher

Bibliographic record

VenueAsian Social Science · 2016
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsImpunityEconomic JusticeLegislatorPopulationFall of manLawPolitical scienceBusinessLegislationPoliticsSociology

Abstract

fetched live from OpenAlex

Aged population of senior citizens is growing noticeably at different regions in the world. Consequently, there are great numbers of demands for healthcare services. One of the services is assistive walking devices which have important role in mobility, stability, walking, and independency of older adults. Although various type of walking devices are available for older adults, yet fall incidents with severe injuries take place. Therefore, it is critical to analyze fall incidents, find out fall factors, and assess walking devices to minimize fall. This paper mainly focuses on risk factors of fall, considerable role of Information and Communication Technology (ICT) in walking devices, and also analyzes fall incidents with the purpose of understanding how fall incidents take place. This paper assists to have a clear understanding about fall incidents and its associated injuries.peration and its successful implementation will be the guarantor of justice and prevent impunity and escape the Probable criminals and suspects to countries far away from the crime scene. In the last two centuries globally in many countries have enacted expanded domestic law in the area of extradition to many extradition treaties and international conventions in this field has joined. Palermo Convention, including the most recent documents that contain comprehensive provisions on extradition and numerous countries has come to join in. In Iran in 1960 Legislator comprehensive and modern law in the area of extradition imposed And many international laws in this area has been invoked and respected. So, we gathered to view the article Ferraro circuit analysis on the world of law and the Palermo Convention and international law in the field of extradition elaborated on. The present article deals with these issues on two separate levels.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.020
GPT teacher head0.372
Teacher spread0.353 · 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 designOther design
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

Citations9
Published2016
Admission routes1
Has abstractyes

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