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Record W2084244869 · doi:10.1177/0013164403258450

Measurement and Validity Characteristics of the Short Version of the Social and Emotional Loneliness Scale for Adults

2004· article· en· W2084244869 on OpenAlexaff
Enrico DiTommaso, Cyndi Brannen, Lisa A. Best

Bibliographic record

VenueEducational and Psychological Measurement · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsLonelinessPsychologyDiscriminant validityConvergent validityUCLA Loneliness ScaleConstruct validityConcurrent validityScale (ratio)Clinical psychologyTest validityDevelopmental psychologyPsychometricsSocial psychologyInternal consistency

Abstract

fetched live from OpenAlex

This article presents a psychometric study of the short form of the Social and Emotional Loneliness Scale for Adults (SELSA-S). Data were collected via self-report measures and mail surveys from several samples including university students, spouses of military personnel, and psychiatric patients. A total of 1,526 individuals took part in this study. Results indicated that the scores from the three scales of the SELSA-S were highly internally reliable. Concurrent validity for the scales was indicated by the statistically significant relationships with other measures of loneliness. Construct (convergent and discriminant) validity was supported by strong relationships with measures of the adequacy of intimate relationships (e.g., attachment and social intimacy) and by the association of the three types of loneliness to measures of social competence, self-esteem, trust, health, and well-being. Finally, results from a factor analysis indicated that the three-factor model of the SELSA-S provided the best fit to the data.

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)0.006
metaresearch head score (Gemma)0.022
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.228
GPT teacher head0.401
Teacher spread0.173 · 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

Citations373
Published2004
Admission routes1
Has abstractyes

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