How can we make better health decisions: a Best Buy for all?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<ns4:p>The World Health Organization (WHO) resolution calling on Member States to work towards achieving universal health coverage (UHC) requires them to prioritize health spending. Prioritizing is even more important as low- and middle-income countries transition from external aid. Countries will have difficult decisions to make on how best to integrate and finance previously donor-funded technologies and health services into their UHC packages in ways that are efficient and equitable, and operationally and financially sustainable.</ns4:p> <ns4:p> The International Decision Support Initiative (iDSI) is a global network of health, policy and economic expertise which supports countries in making better decisions about how best to spend public money on healthcare. In May 2019, iDSI convened a roundtable entitled <ns4:italic>Why strengthening health systems to make better decisions is a Best Buy</ns4:italic> . The event brought together members of iDSI, development partners and other organizations working in the areas of evidence-informed priority-setting, resource allocation, and purchasing. The roundtable participants identified key challenges and activities that could be undertaken by the broader health technology assessment (HTA) community: </ns4:p> <ns4:p>• to develop a new publication package on premium estimation and budgeting, actuarial calculations and risk adjustment, provider payment modalities and monitoring of quality in service delivery</ns4:p> <ns4:p>• to call on the WHO to redouble its efforts in accordance with the 2014 Health Intervention and Technology Assessment (HITA) World Health Assembly resolution to support countries in developing priority setting and HTA institutionalization, and to lead by example through introducing robust HTA processes in its own workings</ns4:p> <ns4:p>• to develop a single Theory of Change (ToC) for evidence-informed priority setting, to be agreed by the major organizations working in the areas of priority setting and HTA.</ns4:p>
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.
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 | 0.055 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 it