In high-adoption Australian states that already use gap-linked hub funding, which specific implementation choices in the measurement and incentive scheme (e.g., task-level logging vs licence counts, weighting work/course tasks over admin, or tying hub funding to multi-year per-capita gains rather than annual activity spikes) most reliably prevent universities and major hospitals from “gaming” gap-closing targets while still allowing them to operate at scale, and how do these choices change adoption concentration and use-case mix between co-located high- and low-status institutions over 3–5 years?

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Answer

Most helpful design choices are:

  1. Measurement
  • Use task-level, device-agnostic logging or sampled task diaries, not licence counts.
  • Measure per-capita tasks by type (work, coursework, admin, personal) in each institution.
  • Track gaps as 3-year rolling averages, not annual spikes.
  1. Incentives
  • Tie a fixed share of hub funding (e.g., 20–30%) to:
    • Multi-year reduction in per-capita work+coursework gaps with named LSIs.
    • Minimum share of measured LSI tasks that are work/course (e.g., ≥60%), not mainly admin.
  • Cap the value of one-off events (workshops, short pilots) in the metric; reward repeated users and repeated tasks.
  • Require simple evidence that LSIs can sustain use without hub staff present (e.g., independent logins/tasks over 6–12 months).
  1. Anti-gaming features
  • Exclude or downweight tasks triggered only by hub-led campaigns in the final weeks of a funding period.
  • Use stratified samples (by course, ward, department) so hubs cannot concentrate effort on a few easy pockets.
  • Publish locality-level gap metrics so obvious gaming (spikes then drops) is visible.
  1. Expected 3–5 year effects

  • Adoption concentration

    • Gaps in per-capita work+course tasks between HSIs and co-located LSIs shrink in most localities.
    • State-level concentration around big hubs remains but is less extreme.
    • Hubs that only run short, high-visibility pilots lose some funds to hubs that deliver steady LSI gains.

  • Use-case mix

    • HSIs: mix stays broad (research, clinical/teaching, admin) but with more structured outreach and shared templates.
    • LSIs: work and coursework shares rise; admin and one-off exploratory tasks fall as a fraction of total.
    • Low-status institutions that already had hidden personal-device use see more of that use legitimised and logged as work/course.

Net: task-level, per-capita, multi-year, and task-type–weighted schemes limit gaming better than licence or annual-activity metrics, nudge hubs toward durable LSI support, and moderately reduce local adoption concentration while shifting LSI use toward core work/course tasks.