Exploring Gender Differences in Psychological Need Satisfaction and University Student Well-Being
Bibliographic record
Abstract
Basic psychological needs (competence, relatedness, and autonomy) play a central role in human flourishing. Self-Determination Theory research consistently shows that the satisfaction of these needs predicts well-being, while frustration predicts ill-being (Ryan & Deci, 2000). Understanding whether these effects operate similarly across social groups is critical for developing policies that effectively support healthy psychological functioning. Although basic psychological need effects have shown to be universal across gender, very little work has tested whether this universality holds when each basic psychological need is tested individually (Ryan & Deci, 2000). Additionally, cross-cultural Self-Determination Theory research has demonstrated significant cultural differences in the strength of well-being outcomes in response to changes in basic psychological need satisfaction (Nalipay et al., 2020). Gender, a cultural construct of its own, may demonstrate similar differences when each basic psychological need is separated during analysis (Andermann, 2010). The proposed study asks whether changes in competence, relatedness, and autonomy predict well-being similarly for men and women over the course of a school year. We predict that increases in satisfaction of any single need will correlate positively with subjective well-being and negatively with controlled motivation, extrinsic aspirations, and ill-being. However, we expect gender to moderate the strength of these associations, challenging the assumption of full universality within Self-Determination Theory. The objectives are to compile a balanced archival dataset using archival longitudinal data from the McGill Human Motivation Lab (2014–2019). Male participants in each study will be matched with female participants, and change in scores for each need will be calculated across the academic year. Hierarchical linear regressions in SPSSX will then test the predictive effects of each need and the moderating role of gender. This study will contribute to the understanding of basic psychological needs, gender, and university student well-being. Additionally, as students are often required to sacrifice satisfaction in one need for the growth of another, this research can guide university institutions to create policies and initiatives that promote the basic psychological needs most central to student well-being (Holding et al., 2020).
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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 | 0.002 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".