MétaCan
Menu
← Back to cohort
Record W2343304224 · doi:10.5539/gjhs.v8n12p87

The Relations between Disability and Environmental Factors: A Pilot Study in Iranian Older Adults

2016· article· en· W2343304224 on OpenAlexvenueno aff
Mohammadreza Shahbazi, Mahshid Foroughan, Rahgozar Mahdi, Reza Salman Roghani

Bibliographic record

VenueGlobal Journal of Health Science · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGerontology

Abstract

fetched live from OpenAlex

OBJECTIVE: This study explored disability and its correlations with the environmental factors in a group of Iranian older adults. MATERIALS & METHODS: A cross sectional study was performed. One hundred participants receiving adult day care services in Kahrizak center in Iran were selected by using the complete enumeration method. The World Health Organization Disability Assessment Schedule 2 (WHODAS II) and the Craig Hospital Inventory of Environmental Factors (CHIEF) questionnaires were used to collect data. RESULTS: The mean score of disability was 20.61±13.66, and the scores were higher in women compared to men (P=0.001). Among the CHIEF-25 items‚ the most frequently perceived barrier by the participants was transportation followed by home design and unavailability of health care services. There was a significant association between the disability scores and the environmental factors (P<0.001). Also, significant relationships were found between the disability and all the subscales investigated in the study (polices‚ physical/structural‚ attitude/support‚ services/assistance) (P<0.001). CONCLUSION: Appropriate transportation‚ availability to health care services and removing physical/structural barriers should be taken in consideration.

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)0.001
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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.368
Teacher spread0.329 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueGlobal Journal of Health Science→Same topicHealth disparities and outcomes→French-language works237,207→