Bibliographic record
Abstract
Abstract This presentation will argue that increased investment in health research is a significant public health imperative. It will use New Zealand as an illustrative case study and also draw upon international experiences. Over the last ten years New Zealand government investment allocated to health research has stood at between 0.6% and 0.8% of government health care costs. Health research advocacy organisation New Zealanders for Health Research (NZHR) argues that the level of investment should be increased to at least 2.4% in order to align with international norms, improve the population's health and well-being and save lives. New Zealand's annual amenable and non-amenable premature mortality currently stands at about 5000 and 7000 deaths respectively. Increased investment in health research holds the key to significantly impacting these figures and should be regarded as an important public health issue. NZHR is one of five similar health research advocacy organisations globally, the others being Research America, Research Canada, Research Sweden and Research Australia. All consistently demonstrate strong public support for their governments to be more actively committed to achieving increased investment in health research. New Zealand is scheduled to have a General Election in September 2020, so the results of NZHR's own polling, supported by that of our sibling organisations, are of particular relevance as NZHR seeks to make health research investment an election issue. The presentation will include comparative information from the five health research advocacy organisations globally, and will note that health researchers irrespective of where they are based are part of an international community where knowledge is shared to the benefit of all nations globally. The presentation therefore will challenge all countries, including New Zealand, to examine, and where necessary address, the adequacy of their own levels of health research investment. Key messages Health research saves lives. Health research investment is a public health issue.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.124 | 0.039 |
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".