What is improvement science, and what makes it different? An outline of the field and its frontiers
Bibliographic record
Abstract
Improvement science has emerged as an interdisciplinary field of enquiry to provide methodological and scientific rigour to the practice and study of improvements in healthcare, and with contributions from a wide range of stakeholders and perspectives. However, compared to more well-established health-related sciences, the science of improvement remains in relative infancy. Whilst the improvement community has grown considerably, there is no existing articulation of the scope of what matters to the health and social care improvement community, and how this aligns to the enquiries of the field of improvement science. This paper aims to outline key areas of interest to the improvement community, and to propose distinguishing features of improvement science that help differentiate it from other areas of enquiry. Two over-arching research questions are identified, along with ten associated areas of enquiry which are grouped into three clusters: (1) improvement in practice, (2) aligning improvement efforts and (3) advancing the contribution of the improvement community. Four features that collectively define and distinguish the field of improvement science are proposed. The outline of the improvement landscape provides a common language for the diverse improvement community, supporting people to transcend disciplinary interests and constraints, and to consider how, collectively, we can improve health and care. Others are invited to refine and advance mapping of the improvement landscape by identifying gaps and increasing contributions from diverse perspectives.
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 | 0.107 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.011 | 0.075 |
| Scholarly communication | 0.034 | 0.040 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".