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Record W4392653223 · doi:10.3389/fpsyt.2024.1364443

Biopsychosocial determinant of quality of life of older adults in Pakistan and Canada

2024· article· en· W4392653223 on OpenAlexaboutno aff
Syeda Shahida Batool, Samra Tanveer, Sarvjeet Kaur Chatrath, Syeda Azra Batool

Bibliographic record

VenueFrontiers in Psychiatry · 2024
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiopsychosocial modelQuality of life (healthcare)GerontologyMedicineScale (ratio)Social supportCross-sectional studyPsychosocialPsychologyPsychiatryNursing

Abstract

fetched live from OpenAlex

Background: The rapidly rising average age of the older adults has brought various global healthcare challenges. A core challenge is how to enhance their quality of life (QoL). Objective: The objective of the current study was to test the significance of biopsychosocial determinants of quality of life of older adults in Pakistan and Canada. Methodology: A cross-sectional survey was carried out on a conveniently approached purposive sample of 1,005 older adults (Pakistani = 557 and Canadian = 448) of age range between 60 years and 80 years. The data were collected via demographic datasheet, World Health Organization Quality of Life Brief Scale, Health and Lifestyle Questionnaire, General Self-Efficacy Scale, Rosenberg Self-Esteem Scale, and Berlin Social Support Scale. Results: = .27, and.68) quality of life of older adults in Pakistan and Canada, respectively, after controlling the demographic variables. Significant differences were found between Pakistani and Canadian older adults on biopsychosocial factors: Canadian older adults scored significantly higher on health and lifestyle, self-efficacy, and quality of life, and older adults in Pakistan scored significantly higher on self-esteem and social support. Conclusion: A significant amount of better QoL of older adults can be achieved through enhancing the biopsychosocial correlates of their QoL, both in Pakistan and Canada.

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)0.000
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 score0.079
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.008
GPT teacher head0.330
Teacher spread0.322 · 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

Citations8
Published2024
Admission routes1
Has abstractyes

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