The General Mattering Scale, the Anti-Mattering Scale, and the Fear of Not Mattering Inventory: Psychometric Properties and Links with Distress and Hope Among Chinese University Students
Bibliographic record
Abstract
Purpose: Mattering is essential to university students' mental health. Feeling valued by others or unimportant can affect their overall well-being. However, most measures for assessing mattering have been developed and tested in Western countries, with limited evaluation of the measures when administered to university students in other regions. This study evaluated the reliability and validity of three mattering-related instruments - the General Mattering Scale (GMS), Anti-Mattering Scale (AMS), and Fear of Not Mattering Inventory (FNMI) among Chinese university students using classical test theory and Rasch analysis. Methods: The study comprised 3594 university students from 19 universities across 13 provinces in mainland China, with a balanced gender distribution of 47.2% females and 52.8% males. Participants' ages ranged from 18 to 37, averaging 20.02 years. Most (78.4%) were in four-year programs, with the rest in three-year programs. The majority were freshmen (54.2%), and 86.3% had siblings. The predominant major was engineering (43.4%), followed by roughly equal representations in science, social science, and literature/art. Results: The three scales showed high reliability and factorial validity, with Rasch analysis confirming their unidimensionality and monotonicity, although 2 of 15 items (one GMS item and one FNMI item) had lower fit. There were no substantial differences in item functioning between male and female respondents. Further analyses indicated that mattering, anti-mattering, and fear of not mattering all explained significant unique variance in levels of hope and distress. Conclusion: All three mattering-related instruments are suitable for assessing Chinese students' mattering, anti-mattering, and fear of not mattering and changes in levels of these mattering dimensions. Moreover, each measure represents a unique element of the mattering construct in terms of associations with levels of hope and distress assessed in during the COVID-19 pandemic.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".