11. Reviews, Trust & Safety
(a) Business requirements
- A customer can leave one review per completed booking (rating 1–5 + free text), tied to a verified, completed, on-platform booking.
- Moderation: reviews enter
pending_moderationand are not public until approved by an admin (or an AI moderator). Aggregate nurse rating/counts are recomputed on every review status transition — publish, hide, reject, unpublish — so hiding a 1-star review never leaves a stale, inflated average. - Low-rating alerting: a rating at or below a configurable threshold (default ≤ 2) with negative content automatically raises a
support_alertsrow for the support team to investigate. - Incident handling: rapid-response protocols with immediate suspension on credible complaints; structured family check-ins and easy in-app concern flagging (the patient is not the sole information source); high-acuity cases routed only to appropriately verified nurses.
(b) Iran-specific considerations
- The buyers are vulnerable people cared for unobserved at home; a single incident can destroy a fragile, trust-first brand — so moderation, low-rating alerting, and immediate suspension are core, not optional.
- Verified-trust is the brand; reviews must be bound to real completed bookings to resist fake-review fraud (gig-marketplace fraud is ~2× elsewhere, mostly impersonation).
(c) MVP vs DEFERRED
- MVP: one-per-completed-booking customer reviews; moderation with full recompute-on-every-transition; low-rating
support_alerts; manual incident suspension. - DEFERRED: two-way (nurse-reviews-customer) double-blind reviews with timed reveal; structured review-tag aggregation (
review_tags_master/review_tag_linksmodeled but a phase-2 nicety); a dedicatedincidentsentity; ML fraud scoring.
(d) Supporting database entities
reviews (moderation status, recompute triggers), review_tags_master, review_tag_links, support_alerts (low-rating, fraud-signal), nurse_profiles (denormalized aggregates), audit_logs.
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