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Window-Aware Scheduling for Health Messaging under Platform Delivery Constraints: Evidence from an AI-Powered WhatsApp Maternal and Infant Health Chatbot in Nigeria — arXiv:2611.02834

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.02834
License (as archived): CC0-1.0
Archived: 2026-11-03T18:23:46.252Z
SHA-256 of the archived text: 090471a60dab7aa29aa12675dae3f75ea94a6a4c82a7a73255e78ecd57c4383c
Stable URL of this page: https://metraipolis.com/documento/dc_ed77cedcd72d

How to cite

Window-Aware Scheduling for Health Messaging under Platform Delivery Constraints: Evidence from an AI-Powered WhatsApp Maternal and Infant Health Chatbot in Nigeria — arXiv:2611.02834 — arXiv (en), https://arxiv.org/abs/2611.02834 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ed77cedcd72d (Metraipolis archive dc_ed77cedcd72d, sha256 090471a60dab7aa29aa12675dae3f75ea94a6a4c82a7a73255e78ecd57c4383c)

BibTeX

@misc{dced77cedcd72d,
  title = {Window-Aware Scheduling for Health Messaging under Platform Delivery Constraints: Evidence from an AI-Powered WhatsApp Maternal and Infant Health Chatbot in Nigeria — arXiv:2611.02834},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.02834},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ed77cedcd72d; sha256 090471a60dab7aa29aa12675dae3f75ea94a6a4c82a7a73255e78ecd57c4383c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.02834. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

WhatsApp lets a business send free-form messages only within 24 hours of a user's last message; outside this customer service window, only pre-approved templates are delivered. Health chatbots inherit this rule, but its cost is rarely measured. We analyse 98,607 messages sent to 3,696 mothers by a WhatsApp maternal health chatbot in Nigeria, with a largely separate export as replication. No free-form message failed with the window open (0 of 35,500), and failure jumped by 93.0 percentage points (95% CI 87.0 to 99.1) at exactly 24 hours. Two flows produced 66.1% of failures, including an offer to speak with a medical professional that, as built, reached 0.3 in 100 mothers whose window had closed. We formalise window-aware scheduling as a decision problem with an action of known zero value. Model-based offline evaluation, with bounds for unconfirmed delivery, estimates that sending the offer as an approved template would reach 81.5 to 86.8 in 100 such mothers, against 16.6 for waiting and 11.6 for a reply prompt. In a calibrated simulation, tabular Q-learning converges to the same template rule and does not beat it in any of 13 stress tests, and opting out does not rise with message volume. The main gain comes from respecting the platform rule, and it needs no learning. All outcomes are deliveries, not health outcomes.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Computation and Language (cs.CL) ; Computers and Society (cs.CY).
Pagina del lavoro: https://arxiv.org/abs/2611.02834 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T18:10:07.981Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).