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Record W7083579929 · doi:10.1016/j.erap.2025.101098

Psychometric properties of the 10-item English-language Social Provisions Scale

2025· article· en· W7083579929 on OpenAlexafffundabout

Bibliographic record

VenueEuropean Review of Applied Psychology · 2025
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsUniversité de Moncton
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health ResearchCanada Foundation for InnovationUniversité de Moncton
KeywordsScale (ratio)Confirmatory factor analysisDimension (graph theory)PsychometricsExploratory factor analysisCriterion validityMental healthReliability (semiconductor)

Abstract

fetched live from OpenAlex

One of the most widely used self-report questionnaires to assess perceived social support is the Social Provisions Scale (SPS; Cutrona & Russell, 1987 ). This 24-item scale was designed to measure six dimensions of social support. A brief, 10-item version of the SPS was developed by Caron (SPS-10; Caron, 2013 ). This study examined the reliability, dimensionality and criterion validity of the English-language SPS-10. SPS-10 data from 19,656 respondents (8,928 men and 10,728 women) of the Canadian Community Health Survey (CCHS) were subjected to exploratory and confirmatory factor analysis. The external validity of the total score and the suggested dimension scores was assessed through correlations with three criterion health variables: psychological distress, positive mental health, and perceived health. Principal component analysis of the SPS-10 revealed a single dimension of social support. Confirmatory factor analyses of various models were also consistent with a unidimensional interpretation of the SPS-10 scores. Although the reliability of the total score was satisfactory (Cronbach’s alpha coefficient of 0.93), the reliabilities of the suggested dimension scores were moderate. The dimension scores do not show evidence of differential validity with respect to the health variables examined. In light of the results of various factor analyses and given the lack of external validity exhibited by the suggested dimensions, SPS-10 scores are best conceptualized as unidimensional. The use of SPS-10 dimension scores is not recommended. L’échelle de provisions sociales (ÉPS; Curtrona et Russell, 1987) est l’un des questionnaires d’auto-évaluation les plus souvent employés pour évaluer le soutien social perçu. Une version brève de ce questionnaire multidimensionnel de 24 items a été mis au point par Caron (2013) : l’ÉPS-10. Cette étude examine la fiabilité, la dimensionnalité et la validité externe de la version anglaise du questionnaire ÉPS-10. Les données de la version anglaise du questionnaire ÉPS-10 de 19 656 répondants (8 928 hommes et 10 839 femmes) de l’Enquête sur la santé dans les collectivités canadiennes (ESCC) sont soumises à l’analyse factorielle exploratoire et confirmatoire. La validité externe du score total et des scores de dimensions proposés est évaluée par la mise en relation avec trois variables de santé : détresse psychologie, santé mentale positive et santé perçue. L’analyse en composante principale révèle une seule dimension de soutien social. L’analyse factorielle confirmatoire de divers modèles est également compatible avec une interprétation unidimensionnelle des scores de l’ÉPS-10. Si la fidélité du score total semble satisfaisante (coefficient alpha de Cronbach de 0,93), celle des scores des dimensions postulées est plutôt faible. Les scores de dimensions ne présentent pas de signes de validité différentielle par rapport aux variables de santé examinées. Compte tenu des résultats des analyses factorielles, et en l’absence de validité différentielle des dimensions, les résultats de la version anglaise du questionnaire ÉPS-10 doivent être conçus comme unidimensionnels ; l’utilisation de scores de dimensions est déconseillée.

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)0.008
metaresearch head score (Gemma)0.024
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 score0.008
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.015
GPT teacher head0.273
Teacher spread0.257 · 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".

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Citations1
Published2025
Admission routes3
Has abstractyes

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