Proceedings of the 2nd Implementation Science Health Conference Australia: Sydney, NSW, Australia, 23-24 March 2023
Bibliographic record
Abstract
In March 2023, the theme of the 2nd Implementation Science Health Conference Australia (ISHCA) was 'Scale.Sustain.Success.' ISHCA has evolved from an annual Implementation Science Symposium, initially hosted by Sydney Health Partners, into a national conference co-hosted by several notable entities concerned with the translation of evidence-based innovation into practice; including the National Health and Medical Research Council (NHMRC) accredited Research Translation Centres, NHMRC funded Centres of Research Excellence, health and medical research institutes, and government health agencies.The conference aimed to enhance the translation, implementation and impact of health and medical research by advancing the field of implementation science and practice within the Australian health care system.Leaders in implementation science and practice were brought together to share successful innovations and learnings on how to implement and sustain evidence-based healthcare improvement at scale.Representatives from clinical practice, academia, management, and policy helped shape the conversations to define the future of implementation science, by hearing from expert speakers, joining conversations, and connecting with colleagues and peers.This supplement summarises the conference proceedings and includes the peer-reviewed abstracts presented.ISHCA is a key national collaborative partnership that transcends Australian state and territories to improve systems of healthcare delivery.
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 | 0.021 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.171 | 0.028 |
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".