{
  "abstract": "Depression has become a serious concern for students worldwide. Aligned with the WHO Helping Adolescents Thrive (HAT) Guidelines and the Social Determinants of Health (SDoH) model, this study isolates five academically relevant factors- academic pressure, work/study hours, study satisfaction, sleep duration, and financial stress- from a dataset of 27,880 university students in India and quantifies their associations with depression. Unlike prior work that maximises classification accuracy, this study prioritises interpretability: logistic regression provides odds ratios (OR) with 95% confidence intervals, Random Forest (RF) and XGBoost rank predictors by feature importance, and SHAP (SHapley Additive exPlanations) values extend the analysis to individual-level risk explanation. Sleep Duration is encoded as an ordinal integer (0-3) preserving its natural order, and Spearman correlations are computed on the original 27,880-row dataset prior to any resampling. SMOTE oversampling was applied exclusively to the training set, and performance was evaluated on the original imbalanced test set (n = 5,576). Both ensemble models achieve approximately 77-78% accuracy and an AUC of 0.845, confirmed by 5-fold pipeline cross-validation (CV AUC [~] 0.843). Academic pressure is the dominant risk factor (OR = 2.271; RF importance = 0.481; mean |SHAP| = 0.174), while study satisfaction (OR = 0.796) and sleep duration (OR = 0.835) are protective. Interaction analysis reveals how depression risk is amplified by low sleep, high financial stress, and extended study hours: under average conditions, the 50% risk threshold is crossed at Academic Pressure {approx} 3.04, and under compounded adverse conditions, this tipping point shifts to even lower pressure values. These findings provide data-driven thresholds aligned with WHO-endorsed modifiable determinants to support early detection and institutional counselling.",
  "abstract_shown": true,
  "affiliations": [
    "University of Barishal"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "T. Ahmed"
    },
    {
      "name": "M. R. A. Asif"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ammmdxkseqvf6.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.09.26360019",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.08.09.26360019v2.full",
  "id": "ammmdxkseqvf6",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.08.09.26360019v2",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "psychiatry and clinical psychology"
    ],
    [
      "openalex",
      "3204|Student Stress and Coping"
    ]
  ],
  "openalex": "W7202156959",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.08.09.26360019v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ammmdxkseqvf6.ris",
  "sources": [
    {
      "first_seen": "2026-11-11T00:21:36Z",
      "native_id": "10.64898/2026.08.09.26360019",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.08.09.26360019v2"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:psychiatry and clinical psychology",
      "subfield": "psychiatry"
    }
  ],
  "title": "Quantifying Academic Risk Factors for Student Depression Using WHO Frameworks: Odds Ratios, SHAP Explainability, and Tipping Point Analysis",
  "title_text": "Quantifying Academic Risk Factors for Student Depression Using WHO Frameworks: Odds Ratios, SHAP Explainability, and Tipping Point Analysis",
  "type": "preprint",
  "updated_at": "2026-11-12T00:20:32Z",
  "url": "https://research.draconitic.com/paper/ammmdxkseqvf6",
  "venue": "medRxiv",
  "versions": []
}