SP3-74 Expedited approach to disseminating evidence to policy makers in order to improve Aboriginal child health and well-being in Canada
Bibliographic record
Abstract
Objectives To facilitate timely uptake of research evidence by policy makers and support the implementation of evidence-informed policies and practices to improve Aboriginal child health and well-being. Methods This work was initiated in response to national government interest in improving Aboriginal child health and well-being. The approach began with a synthesis review which critically and culturally appraised published papers to identify promising practices. Next, a summary of the synthesis and other relevant reports was written for stakeholders and five recommendations were developed. The research team and other researchers in the area validated these documents. Key stakeholders, including policy makers, community leaders, and content experts, were surveyed to assess general support for the recommendations, identify other key contacts, and identify facilitators and barriers to dissemination. With support from stakeholders and the lead organisation's board of directors, the recommendations were finalised as a brief “Call To Action” document. An Aboriginal community member also wrote a culturally aligned, plain language version. Results In 6 months, peer-supported recommendations were developed and broadly disseminated to stakeholders locally and nationally. The “Call To Action” was distributed to stakeholders via facilitated discussions, presentations, email, and internet. A follow-up survey of stakeholders will be conducted to assess the impact of our dissemination approach. The intended outcomes will include increased awareness, knowledge, and investment in evidence-informed strategies as recommended in the “Call To Action”. Conclusions The approach undertaken provided timely research evidence for policy makers. Other than raising awareness, the impact of this approach remains to be determined.
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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 | 0.174 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.020 | 0.004 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.022 | 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".