From research to clinical practice: Considerations in moving research into people's hands. Personal reflections that may be useful to others
Bibliographic record
Abstract
It may take many years for published clinical research findings to be found, understood, adopted and applied in practice. In recognition of this delay, many jurisdictions and agencies are now promoting a stronger link between research and its dissemination in useable forms that will enable practitioners to access, understand and use new ideas. The purpose of this paper, first presented as a keynote address at the 15th Annual Meeting of the European Academy of Childhood Disability in Oslo in October 2003, is to share experiences of the author and his colleagues at a childhood disability system-linked research centre in Ontario, Canada. The lessons learned include the value of striving to describe one's findings in plain language; writing study reports for parents and children who are involved in research studies; using multiple methods to disseminate one's work; and making explicit the potential importance and applicability of the findings to readers of the work. Engaging end users at many stages of the development and field testing of one's work will enhance buy-in and lend added credibility to the work, as well as influencing content and process as the research unfolds. The result is likely to be greater recognition of 'familiar' aspects of the research and the adoption of relevant findings.
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 | 0.282 | 0.567 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.012 | 0.062 |
| Scholarly communication | 0.038 | 0.079 |
| Open science | 0.010 | 0.028 |
| Research integrity | 0.038 | 0.082 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
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".