Te Ara Ako o Collaborative Aotearoa Study Tour: Shared learnings from Canada and England - Integrated Primary and Community Care and Indigenous Leadership Shared Leanings
Bibliographic record
Abstract
Collaborative Aotearoa hosted an international study tour during May 2023, after our 2020 plans were paused by Covid19. Our study tour delegates from the health and social sector visited several countries to learn from other high performing, innovative primary and community care systems. Our destinations included Alberta, Ontario, London, and Belgium. The presentation will focus on sharing the learnings about integrated care systems including a focus on indigenous leadership. The study tour learnings are supporting our leaders in Aotearoa, New Zealand in the redesign of our primary and community care systems. Specifically aiming to deliver on the vision of achieving Hauora, health and wellbeing for all New Zealanders. We will share fresh perspectives and experiences that support the health reforms and multi sector collaboration that delivers real community systems change from an indigenous lens. The key objectives of the study tour included extending our learning with a focus on: ▪ Strategies and activities around indigenous leadership and improving equity of outcomes for our vulnerable populations. ▪ Using innovative workforce models and comprehensive primary and community team-based care models to support transformational change. ▪ How to leverage community assets and community voice to plan and extend the reach of services. ▪ How to use population health data to effectively understand the needs of priority populations. ▪ How primary and community services integrate to provide holistic care (including primary mental health care) ▪ Using extended care teams to the top of their scope to address the wider social determinants of health. The structure of the session will be interactive to prompt discussion and reflection relating to our Study Tour.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.006 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".