MétaCan
Menu
Back to cohort
Record W1944544483 · doi:10.5539/gjhs.v8n7p68

Evaluation of the Criteria for Quality of Life of Elderly Health Care Centers in Tehran Province, Iran

2015· article· en· W1944544483 on OpenAlexvenueno aff
Fereshteh Farzianpour, Mohammad Arab, Abbas Rahimi Foroushani, Esmaeil Morad Zali Mehran

Bibliographic record

VenueGlobal Journal of Health Science · 2015
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
FundersTehran University of Medical Sciences and Health Services
KeywordsMedicineDemographyQuality of life (healthcare)PopulationCross-sectional studyGerontologyHealth careEnvironmental healthNursing

Abstract

fetched live from OpenAlex

BACKGROUND & OBJECTIVES: The objective of this study was to evaluate the elderly quality of life of people covered by the healthcare centers in Tehran and its influencing demographic and background factors. METHOD: This is a cross-sectional study of quality of life of the elderly population covered by healthcare centers and bases in Tehran, as well as the influential background and demographic factors. Sampling was performed using simple random stratified sampling proportionate to the size of strata. Data were collected using the Iranian version of the standard questionnaire Short Form Health Survey (SF-36). RESULTS: According to the findings, 240 (60%) of the cases were men and 160 (40%) were women. Regarding age distribution, 76.3% fell in the 60-69 age range and 87.2% were illiterate. 18% of the elderly stated that they have financial problems and 19.5% did not express any financial problems. While studying the relationship between financial status and health status with the mean scores of quality of life, statistically significant differences were observed in all domains (p=0.032<0.001). The mean quality of life was lower in women compared to men. CONCLUSIONS: The findings of the present study indicate that the health-related quality of life in the elderly population is influenced by their health status and demographic and background variables.

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)0.022
metaresearch head score (Gemma)0.001
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 score0.176
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.180
GPT teacher head0.515
Teacher spread0.335 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueGlobal Journal of Health ScienceSame topicHealth and Well-being StudiesFrench-language works237,207