Bibliographic record
Abstract
With a significant growth in cost, and the growing demand of our healthcare systems as a result of populations living longer, there is growing recognition that a healthcare system based on deficit focused models and the treatment of symptoms is inadequate. Whilst health research is continually focussed on the cure of conditions and the management of health, and rightly so, there is also a move to target environment, cultural and economic factors in population health and wellbeing (Hanlon and Carlise 2012). Exploring the Interconnectivity of health to other systems, we used the Manoa Method (Schulz 2015) in a workshop delivered at the Design + Health Symposium at Auckland University of Technology, New Zealand in September 2019, to map out the future of healthcare as a system connected to social, political, economic, science/technology and environmental factors. Participants included designers, healthcare practitioners and technologists from across New Zealand, Australia and Canada. Considering a landscape 20-30 years from now, the groups worked to identify and state a future scenario or trend (for example ‘earning healthcare by points’) related to each factor, and brainstormed 5-7 primary impacts, related to their chosen scenario. They then explored further associated impacts and mapped the overall connectivity. This paper summarises the themes of each factor and discusses the future scenarios envisioned by the groups and their associated impacts to health. The paper also includes the mapping diagrams developed by participants, provoking questions, conversations and possible actions, such as ‘how would your organisation or community change to thrive in each scenario?’
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.013 | 0.026 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 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".