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Record W2378578582

Evaluation and its relevant factors of psychological scale among 122 empty-nesters

2014· article· en· W2378578582 on OpenAlexaboutno aff
Yang Yong-fen

Bibliographic record

VenueZhiye yu jiankang · 2014
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLonelinessHappinessDepression (economics)UCLA Loneliness ScaleQuality of life (healthcare)Significant differenceRecreationScale (ratio)Geriatric Depression ScaleHamdMedicineGerontologyPsychologyDemographyPsychiatryDepressive symptomsAnxietyGeographyInternal medicineEcologySocial psychologySociologyNursing
DOInot available

Abstract

fetched live from OpenAlex

[Objective]To investigate the happiness,depression and loneliness of empty-nesters,analyze the influencing factors,provide the basis for improving the life quality of empty-nesters. [Methods]112 empty-nesters were investigated by Memorial University of Newfoundland Scale of Happiness /Geriatric Depression Scale /UCLA Loneliness Scale( MUNSH /GDS /UCLE) and general condition questionnaire,and the influencing factors were analyzed. [Results]The scores of happiness,depression and loneliness among 112 empty-nesters were( 36. 58 ± 6. 87),( 11. 00 ± 2. 89) and( 30. 58 ± 7. 29). The differences of score of MUNSH /GDS /UCLE in different monthly income and recreational activities frequency were statistically significant( P 0. 05). The difference of UCLA score in different physical exercise frequency was statistically significant( P 0. 05).[Conclusion]The condition of low happiness,mild depression and moderate loneliness among empty-nesters shouldn't be ignored. Monthly income,recreational activities frequency and physical exercise frequency are the key factors to improve the life quality of empty-nesters.

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)0.001
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 score0.127
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.057
GPT teacher head0.391
Teacher spread0.334 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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