Higher levels of self-reported quality of student life predict a lower risk for high school dropout among seventh-grade students
Bibliographic record
Abstract
High school dropout is a national concern, which often leads to long-term adverse consequences. For example, students who drop out of high school are more likely than those with higher levels of educational attainment to be living in poverty, to be in need of public assistance, and to be incarcerated. Most research focuses on individual and contextual (unalterable) factors associated with dropout instead of focusing on factors that are more responsive to change such as the quality of student life (QOSL). The purpose of this study was to better understand how quality of student life, as measured by four sub-scales of a QOSL questionnaire (i.e., satisfaction, well-being, social belonging & empowerment/control), may predict risk for high school dropout. More specifically, this study assessed risk for dropout and QOSL in 34 students in 7th grade aged 11 to 13 years who were attending a school in Southern Québec, Canada. The results indicate that the potential risk for dropout decreases as students report greater levels of overall QOSL. More specifically, satisfaction and well-being (components of QOSL) were found to contribute the most to the potential risk for dropout. Additionally, students with higher socio-economic status (SES) reported greater levels of QOSL and lower risk for dropout. This study lends support to the idea that even at a high school level, administrators and teachers can make changes in their policies and practices that improve QOSL and increases the likelihood that students will earn their high school diploma.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".