Accomplishing reform: successful case studies drawn from the health systems of 60 countries
Bibliographic record
Abstract
Braithwaite, J., Mannion, R., Matsuyama, Y., Shekelle, P., Whittaker, S., Al-Adawi, S., ... & Hughes, C. (2017). Accomplishing reform: successful case studies drawn from the health systems of 60 countries. International Journal for Quality in Health Care, 29(6), 880-886.Link to full textAbstractHealthcare reform typically involves orchestrating a policy change, mediated through some form of operational, systems, financial, process or practice intervention. The aim is to improve the ways in which care is delivered to patients. In our book 'Health Systems Improvement Across the Globe: Success Stories from 60 Countries', we gathered case-study accomplishments from 60 countries. A unique feature of the collection is the diversity of included countries, from the wealthiest and most politically stable such as Japan, Qatar and Canada, to some of the poorest, most densely populated or politically challenged, including Afghanistan, Guinea and Nigeria. Despite constraints faced by health reformers everywhere, every country was able to share a story of accomplishment-defining how their case example was managed, what services were affected and ultimately how patients, staff, or the system overall, benefited. The reform themes ranged from those relating to policy, care coverage and governance; to quality, standards, accreditation and regulation; to the organization of care; to safety, workforce and resources; to technology and IT; through to practical ways in which stakeholders forged collaborations and partnerships to achieve mutual aims. Common factors linked to success included the 'acorn-to-oak tree' principle (a small scale initiative can lead to system-wide reforms); the 'data-to-information-to-intelligence' principle (the role of IT and data are becoming more critical for delivering efficient and appropriate care, but must be converted into useful intelligence); the 'many-hands' principle (concerted action between stakeholders is key); and the 'patient-as-the-pre-eminent-player' principle (placing patients at the centre of reform designs is critical for success).PMID: 29036604
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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 | 0.034 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.031 | 0.021 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".