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Record W2564790565

A Psychological Appraisal of Pre-Retirement Anxiety Among Some Selected Workers in Lagos Metropolis

2016· article· en· W2564790565 on OpenAlexvenueno aff
Odunayo T. Arogundade

Bibliographic record

VenueStudies in sociology of science · 2016
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsAnxietyPsychologyEmotional intelligenceClinical psychologySelf-efficacyScale (ratio)Test (biology)Developmental psychologySocial psychologyPsychiatry
DOInot available

Abstract

fetched live from OpenAlex

This study presents the findings on workers reaction to retirement in Lagos metropolis. A total number of eight hundred (800) participants (M=400, F= 400) were randomly selected from both private and public sector organisations in Lagos metropolis. The data were collected using a pre-retirement anxiety scale (PAS) developed, standardized and validated by the researcher along side with emotional intelligence and self-efficacy scales. Simple percentages, independent t test and linear regression analyses were used for analysed the data. Some of the findings include: a) Workers classified as possessing low emotional intelligence and self efficacy reported higher pre-retirement anxiety compared to their counterparts who possessed high emotional intelligence and self efficacy. b) There were inverse relationships between emotional intelligence and pre-retirement anxiety on the one hand and self-efficacy and pre-retirement anxiety on the other hand. c) Workers’ levels of emotional intelligence and self-efficacy were important predictors of pre-retirement anxiety as they both accounted for 32% variance in pre-retirement anxiety. The implications of the findings in terms of minimizing pre-retirement anxiety were discussed and recommendations were made accordingly.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.041
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.098
GPT teacher head0.466
Teacher spread0.368 · 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.

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

Citations3
Published2016
Admission routes1
Has abstractyes

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