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Record W4399055982 · doi:10.1177/07342829241255232

When Adding One Questionnaire Item Makes a Difference: Representing the Theme of Feeling Cared About in the Expanded General Mattering Scale (The GMS-6)

2024· article· en· W4399055982 on OpenAlexafffund
Gordon L. Flett, Taryn Nepon

Bibliographic record

VenueJournal of Psychoeducational Assessment · 2024
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsYork University
FundersCanada Research Chairs
KeywordsPsychologyFeelingScale (ratio)Theme (computing)Social psychologyDevelopmental psychologyClinical psychologyApplied psychologyCognitive psychology

Abstract

fetched live from OpenAlex

Converging lines of evidence suggest that a feeling of being cared for and cared about is a key element of the feeling of mattering to other people. In the current article, we summarized theoretical observations and the findings of research investigations that indicate that the feeling of being cared about is central to the mattering construct. We then evaluated the role of feeling cared for in an extended six-item General Mattering Scale (GMS-6). A sample of 276 university students completed the GMS-6 and self-report measures of depression and loneliness. Psychometric tests established that a six-item version has one factor and enhanced internal consistency. Correlational analyses confirmed that mattering is associated negatively with depression and loneliness. Hierarchical regression analyses indicated that the additional focus on feeling cared about predicts unique variance in depression and loneliness beyond the considerable amount of variance predicted by the original GMS. The lack of feeling cared as measured by the GMS-6 was established as especially relevant to loneliness. Our discussion focuses on key directions for future research and for the need for a greater emphasis on caring as part of mattering from a construct validity perspective.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.337
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.059
GPT teacher head0.423
Teacher spread0.364 · 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 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

Citations12
Published2024
Admission routes2
Has abstractyes

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