* 2012 The Author(s). This article is published with open access at Springerlink.com The Right Kind of Evidence—Integrating, Measuring, and Making It Count in Health Equity Research
Bibliographic record
Abstract
ABSTRACT As health equity researchers, we need to produce research that is useful, policy-relevant, able to be understood and applied, and uses integrated knowledge translation (KT) approaches. The Manitoba Centre for Health Policy and its history of working with provincial government as well as regional health authorities is used as a case study of integrated KT. Whether or not health equity research “takes the day” around the decision-making table may be out of our realm, but as scientists, we need to ensure that it is around the table, and that it is understood and told in a narrative way. However, our conventional research metrics can sometimes get in the way of practicality and clear understanding. The use of relative rates, relative risks, or odds ratios can actually be detrimental to furthering political action. In the policy realm, showing the rates by socioeconomic group and trends in those rates, as well as incorporating information on absolute differences, may be better understood intuitively when discussing inequity. Health equity research matters, and it particularly matters to policy-makers and planners at the top levels of decision-making. We need to ensure that our messages are based on strong evidence, presented in ways that do not undermine the message itself, and incorporating integrated KT models to ensure rapid uptake and application in the real world.
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.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.397 | 0.218 |
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".