When Adding One Questionnaire Item Makes a Difference: Representing the Theme of Feeling Cared About in the Expanded General Mattering Scale (The GMS-6)
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".