{
  "id": 402,
  "url": "https://arxiv.org/abs/2606.31532v1",
  "title": "A time-series classification framework for individual-level absenteeism prediction under severe class imbalance",
  "summary": "Staff absenteeism imposes substantial operational costs in high-demand work environments such as healthcare, emergency services, meat processing, construction, and courier and delivery services, where proactive workforce planning depends on reliable individual-level absence prediction. Existing regression and classification approaches share a structural limitation; they map features observed at time t to labels at the same time t, reproducing already-realised outcomes rather than predicting futu",
  "authors": "Kwong Ho Li, Matthew Roughan, Wathsala Karunarathne",
  "category": "research",
  "topics": "jobs-economy,healthcare,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-30T11:44:52.000Z",
  "fetched_at": "2026-07-14T14:14:32.644Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/402",
  "original_url": "https://arxiv.org/abs/2606.31532v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}