Engaging the public in healthcare decision making: Should the Emergency Department Treat Everyone who Presents for Treatment?:Citizens' Jury on Emergency Care Services
Bibliographic record
Abstract
Citizens’ Juries (CJ) offer a way of seeking informed public views using a democratic, deliberative process. This report describes the methods, processes, and verdicts of a CJ held in Queensland in June 2012, focussing on public preferences around the provision of emergency care services. This CJ was undertaken as part of larger research study led by Griffith University and funded by an Australian Research Council Linkage Grant, along with partner investigators Queensland Health, Southern Adelaide Local Health Network Inc., the National Institute for Health and Clinical Excellence, Flinders University, and Queensland University of Technology. The larger project aims to facilitate the identification and application of optimal methods for engaging the public in healthcare decision-making, provide guidance on the appropriate population groups to consider when eliciting preferences, and provide direct public input to guide health policy. The project is using two methods to engage the public and address a range of methodological questions: the deliberative CJ and the Discrete Choice Experiment (DCE). The DCE is a quantitative method that can elicit the relative strength of preference of the public around a priority-setting topic, and the trade-offs the public are prepared to make. The electoral roll, obtained with approval from the Electoral Commission of Queensland, was used to develop a sampling frame of the Metro South Health Service District, 1 and a random sample was invited to express interest in being a juror. From those interested, a jury of 22 was purposively selected to reflect the demographic characteristics of Queensland.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".