Profiles of Basic Psychological Needs Satisfaction and Sociodemographic and School-Associated Factors
Bibliographic record
Abstract
The fulfillment of basic psychological needs (competency, autonomy, relatedness) enhances intrinsic motivation, school engagement, and academic achievement. Establishing profiles of psychological needs satisfaction using a person-centered approach may help in identifying students at-risk of low academic success. This study investigated how students vary in their basic psychological needs satisfaction and their associated sociodemographic and school-related factors. Using a latent class analysis approach, five profiles of students were identified at the end of elementary school (n = 1,362). A multinomial regression analysis revealed that academic achievement and the quality of relationship with peers and teachers distinguished most profiles of basic psychological needs satisfaction. In particular, having conflict with teachers increased the likelihood of belonging to profiles with low psychological needs satisfaction. Closeness with the teacher and friendship decreased the likelihood of having average to low levels of psychological needs satisfaction. Students with higher grades in French and Math were less likely to belong to profiles with low competency, autonomy, and/or relatedness satisfaction. Sociodemographic factors, having negative relationship and joint negative behaviors with peers did not distinguish profiles of basic psychological needs satisfaction.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".