Statistical Analysis of Mental Health Factors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study aims to build a model that effectively predicts the presence of depression in 25,113 Canadian citizens ages 15 and over living in the ten provinces. Predictors were chosen for their potential to introduce significant stress into the lives of individuals. The tested variables obtained from the Canadian Community Health Survey (CCHS) of 2012 include current age, sex, marital status, BMI, frequency of alcohol consumption, presence of cancer, drug use, employment status, originating from Canada, and current school enrollment. These variables were subjected to a forward selection process in SPSS to fit a statistically significant binomial logistic regression model. The relationship between individual variables and depression was also examined through the Crosstabs function in SPSS. Results suggested that all chosen explanatory variables have a significant association with the response variable except presence of cancer, which also did not show significance in the final fitted model. Key Terms: Mood disorder, Depression, Mental Health, Canada Discipline: Statistics Faculty Mentor: Dr. Karen Buro
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it