Divvying Up the Funding Pie: What Share for Health Services and Policy Research?
Bibliographic record
Abstract
. 1 Even after the contributions of other important federal funders of health services research are accounted for, the increase remains impressive -from a baseline of about $8 million pre-CIHR to $44.1 million in 2005/06. 2 This massive growth in federal support should warm the hearts of health services researchers and all those who believe that a vastly expanded pool of relevant research evidence is needed to inform health policy development and health system management in the public interest. However, viewed in the context of the distribution of CIHR funding across its four research themes -biomedical; clinical; health systems and services; and population and public health -the picture is less comforting.
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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 | 0.057 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.038 | 0.040 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.028 | 0.012 |
| Insufficient payload (model declined to judge) | 0.042 | 0.006 |
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".