The Development of the French Version of the Psychological Sense of School Membership (PSSM) Questionnaire: An Analysis of its Structure, Properties and Potential for Research with at-risk Students
Bibliographic record
Abstract
Introduction. As it plays an important role in students' adjustment, and positively impacts their motivation and academic success, school belonging seems to be a pivotal determinant of the overall quality of a school experience. However, measuring such a belonging and estimating its contribution to the overall quality of school adjustment remain a challenge for the scientific community. Method. Thus, the French version of the Psychological Sense of School Membership (PSSM) questionnaire was tested to determine its latent structure, validity, and capacity to predict dropout among at-risk students. In Study 1, the French version of the PSSM scale was thoroughly analyzed for validity while performing exploratory factor analysis, confirmatory factor analysis, and multigroup confirmatory factor analysis on self-reported data provided by a sample of high school students. In study 2, answers of a particular sample of at-risk students were carefully analyzed with ANOVAS to determine the potential of the PSSM to predict high school dropout. Results. The exploratory factor analysis and the confirmatory factor analysis revealed four predominant dimensions: (1) teacher-student relationships; (2) peers' relationships; (3) sense of acceptance; and (4) sense of attachment, while the multigroup confirmatory factor analysis revealed the PSSM to be partially invariant with regards to the gender of the participants. In Study 2, we found that the PSSM can be used as a tool to help identify students who are at risk of dropping out of school. Conclusion. Strategies to develop students' school belonging are discussed.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".