Meeting the Challenge of the "Know-Do" Gap Comment on "CIHR Health System Impact Fellows: Reflections on ‘Driving Change’ Within the Health System"
Bibliographic record
Abstract
Bridging the 'know-do' gap is not new but considerably greater attention is being focused on the issue as governments and research funders seek to demonstrate value for money and impact on policy and practice. Initiatives like the Canadian Institutes of Health Research (CIHR) Health System Impact (HSI) Fellowship are therefore both timely and welcome. However, they confront major obstacles which, unless addressed, will result in such schemes remaining the exception and having limited impact. Context is everything and as long as universities and research funders privilege peer-reviewed journal papers and traditional measures of academic performance and success, novel schemes seeking to break down barriers between researchers and end users are likely to have limited appeal. Indeed, for some academics they risk being career limiting. The onus should be on universities to welcome greater diversity and nurture and value a range of academic researchers with different skills matched to the needs of applied health system research. One size does not fit all and adopting a horses for courses approach would go a long way to solving the conundrum facing higher education institutions. At the same time, researchers need to show greater humility and acknowledge that scientific evidence is only one factor shaping policy and practice. To help overcome a risk of ideology and opinion triumphing over evidence, attention should be devoted to encouraging citizens to get actively involved in research. Research funders also need to give higher priority to how policy can be made to stick if the 'know-do' gap is to be closed.
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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 | 0.036 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.028 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.012 | 0.009 |
| Research integrity | 0.078 | 0.108 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".