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Record W2327936520 · doi:10.5430/jnep.v6n8p112

Factors affecting adjustment to retirement among retirees’ elderly persons

2016· article· en· W2327936520 on OpenAlexvenueno aff
Doaa El Sayed Fadila, Raefa Refaat Alam

Bibliographic record

VenueJournal of Nursing Education and Practice · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsMarital statusScale (ratio)GerontologyPsychologyDescriptive statisticsMedicineEnvironmental healthGeography

Abstract

fetched live from OpenAlex

Background and aim : The loss of work through retirement is one of the major adjustments for individual ages. For many, this is the first indicator of the impact of aging. Aim of the study was to identify factors affecting adjustment to retirement among retirees' elderly persons. Method : Cross-sectional descriptive design was adopted. This study was conducted at Waiting Lounge of El Ahly Bank, Nasser Bank, and Faculties of Commerce, Medicine, Science and Veterinary affiliated to Mansoura University, Dakahlia Governorate, Egypt. 210 retirees' elderly person who represented the participants of the current study retired since one year and more. Data was collected using Self-Administered Questionnaire, Retirement Adjustment Scale, and Retirement Resources Inventory. Results: None of the retirees' elderly (100.0%) attended a preparation program for retirement. The total mean score of retirement adjustment scale correlated significantly and positively to the total mean score of the physical, financial, social, and mental resources. Additionally, there is a significant relation between preparation for retirement and the total mean score of retirement adjustment scale. Conclusion: It can be concluded that adjustment to retirement was affected by the retirees’ gender, marital status, level of education, type of job before retirement, job condition, and place of work. In addition, adequate resources as physical, financial, social support and mental capacity are associated with better adjustment to retirement.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.534
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.339
GPT teacher head0.515
Teacher spread0.176 · 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 designQualitative
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

Citations24
Published2016
Admission routes1
Has abstractyes

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