A New Framework and Practice Center for Adapting, Translating, and Scaling Evidence-Based Health/Wellness Programs for People With Disabilities
Bibliographic record
Abstract
Supporting the transition of people with newly acquired and existing disability from rehabilitation into community-based health/wellness programs, services, and venues requires rehabilitation professionals to build evidence by capturing successful strategies at the local level, finding innovative ways to translate successful practices to other communities, and ultimately to upgrade and maintain their applicability and currency for future scale-up. This article describes a knowledge-to-practice framework housed in a national resource and practice center that will support therapists and other rehabilitation professionals in building and maintaining a database of successful health/wellness guidelines, recommendations, and adaptations to promote community health inclusion for people with disabilities. A framework was developed in the National Center on Health, Physical Activity and Disability (NCHPAD) to systematically build and advance the evidence base of health/wellness programs, practices, and services applicable to people with disabilities. N-KATS (NCHPAD Knowledge Adaptation, Translation, and Scale-up) has 4 sequencing strategies: strategy 1-new evidence- and practice-based knowledge is collected and adapted for the local context (ie, community); strategy 2-customized resources are effectively disseminated to key stakeholders including rehabilitation professionals with appropriate training tools; strategy 3-NCHPAD staff serve as facilitators assisting key stakeholders in implementing recommendations; strategy 4-successful elements of practice (eg, guideline, recommendation, adaptation) are archived and scaled to other rehabilitation providers. The N-KATS framework supports the role of rehabilitation professionals as knowledge brokers, facilitators, and users in a collaborative, dynamic structure that will grow and be sustained over time through the NCHPAD.Video abstract available for additional insights from the authors (see Video, Supplemental Digital Content 1, http://links.lww.com/JNPT/A130).
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 | 0.225 | 0.154 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.010 | 0.036 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".