Community engagement and knowledge translation: Progress and challenge in autism research
Bibliographic record
Abstract
The last decade has seen significant growth in scientific understanding and public awareness of autism. There is still a long road ahead before this awareness can be matched with parallel improvements in evidence-based practice. The process of translating evidence into community care has been hampered by the seeming disconnect between the mainstream scientific research agenda and the immediate priorities of many communities. The need for community engagement in the process of translating knowledge into impact has been recognized. However, there remains little consensus or empirical data regarding the process of such engagement and how to measure its impact. We shed light on a number of engagement models and tools, previously advocated in health research, as they apply to autism research. Furthermore, we illustrate the utility of such tools in supporting identification of knowledge gaps and priorities, using two community-based case studies. The case studies illustrate that information generated from research is indeed relevant and critical for knowledge users in the community. Simple and systematic methods can support the translation and uptake of knowledge in diverse communities, therefore enhancing engagement with research and bridging research findings with immediate community needs.
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 | 0.454 | 0.520 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.021 | 0.062 |
| Scholarly communication | 0.049 | 0.065 |
| Open science | 0.011 | 0.075 |
| Research integrity | 0.018 | 0.018 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".