Applying the RE-AIM Framework to Inform the Development of a Multiple Sclerosis Falls-Prevention Intervention
Bibliographic record
Abstract
Successfully addressing the problem of falls among people with multiple sclerosis (MS) will require the translation of research findings into practice change. This process is not easy but can be facilitated by using frameworks such as RE-AIM during the process of planning, implementing, and evaluating MS falls-prevention interventions. RE-AIM stands for Reach, Effectiveness, Adoption, Implementation, and Maintenance. Since its initial publication in 1999, the RE-AIM framework has become widely recognized across a range of disciplines as a valuable tool to guide thinking about the development and evaluation of interventions intended for widespread dissemination. For this reason, it was selected by the International MS Falls Prevention Research Network to structure initial discussions with clinicians, people with MS, and representatives of professional and MS societies about the factors we need to consider in the development of an MS falls-prevention intervention for multisite testing that we hope will someday be disseminated widely. Through a combination of small-group work and large-group discussion, participants discussed four of the five RE-AIM elements. A total of 17 recommendations were made to maximize the reach (n = 3), adoption (n = 5), implementation (n = 4), and maintenance (n = 5) of the intervention the Network is developing. These recommendations are likely to be useful for any MS rehabilitation researcher who is developing and testing interventions that he or she hopes will be widely disseminated.
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 | 0.259 | 0.180 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".