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Record W2553819087 · doi:10.5539/res.v8n4p167

Examining the Effectiveness of Social Skills Training on Loneliness and Achievement Motivation among Nurses

2016· article· en· W2553819087 on OpenAlexvenueno aff
Fatemeh Khosravi Saleh Baberi, Zahra Dasht Bozorgi

Bibliographic record

VenueReview of European Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsnot available
Fundersnot available
KeywordsLonelinessPsychologyFeelingSocial skillsTest (biology)Analysis of covarianceMedical educationDevelopmental psychologySocial psychologyMedicine

Abstract

fetched live from OpenAlex

This study tries to examine the effectiveness of social skills training on feeling of loneliness and achievement motivation in nurses. The present research is an experimental study of pre-test and post-test design with a control group. The research instruments included the revised UCLA Loneliness Scale and the Achievement Motivation Test for adults. The sample size consisted of 40 nurses working in Imam Khomeini Hospital in the city of Ahwaz selected through multi-stage random sampling and assigned to two experimental and control groups. To this end, prior to teaching the social skills, both groups were pre-tested. Then, the experimental group received social skills training for 12 fifty-minute sessions but no training was provided to the control group. Upon the completion of the training course, both groups immediately took the post-tests. The results of one-way analysis of covariance (ANCOVA) and multivariate analysis of covariance (MANCOVA) showed that social skills training significantly increased achievement motivation and reduced feeling of loneliness in nurses.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0010.000

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 head0.106
GPT teacher head0.398
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2016
Admission routes1
Has abstractyes

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