Members' of Parliament knowledge of and attitudes toward health research and funding
Bibliographic record
Abstract
BACKGROUND: Establishment of the Canadian Institutes of Health Research (CIHR) in 2000 resulted in increased funding for health research in Canada. Since 2001, the number of proposals submitted to CIHR that, following peer review, are judged to be of scientific merit to warrant funding, has grown by 77%. But many of these proposals do not receive funding because of budget constraints. Given the role of Members of Parliament in setting government funding priorities, we surveyed Members of Parliament about their knowledge of and attitudes toward health research, health research funding and CIHR. METHODS: All Members of Parliament were invited to participate, or to designate a senior aide to participate, in a 15-minute survey of knowledge of and attitudes toward health research, health research funding and CIHR. Interviews were conducted between July 15, 2006, and Dec. 20, 2006. Responses were analyzed by party affiliation, region and years of service as a Member of Parliament. RESULTS: A total of 101 of 308 Members of Parliament or their designated senior aides participated in the survey. Almost one-third of respondents were senior aides. Most of the respondents (84%) were aware of CIHR, but 32% knew nothing about its role. Participants believed that health research is a critical component of a strong health care system and that it is underfunded. Overall, 78% felt that the percentage of total government spending directed to health research funding was too low; 85% felt the same way about the percentage of government health care spending directed to health research. Fifty-four percent believed that the federal government should provide both funding and guidelines for health research, and 66% believed that the business sector should be the primary source of health research funding. Participants (57%) most frequently defined health research as study into cures or treatments of disease, and 22% of participants were aware that CIHR is the main federal government funding organization for health research. Participants perceived health research to be a low priority for Canadian voters (mean ranking 3.8/10, with 1 being unimportant and 10 being extremely important [SD 1.85]). INTERPRETATION: Our results highlight significant knowledge gaps among Members of Parliament regarding health research. Many of these knowledge gaps will need to be addressed if health research is to become a priority.
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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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".