What’s behind the Dashboard? Intervention Mapping of a Mobility Outcomes Monitoring System for Rehabilitation
Bibliographic record
Abstract
Training and follow-up for older adults who received new assistive technology can improve device use adoption and function, but there is a lack of systematic and coordinated services. To address this gap, the Internet-based MOvIT+™ was designed to provide remote monitoring and support for assistive technology users and their caregivers. This paper presents the intervention mapping approach that was used. In step 1, we established a project governance structure and a logic model emerged from interviews with stakeholders and a systematic review of literature. In step 2, a modified TRIAGE consensus process led to the prioritization of thirty-six intervention components. In step 3, we created use cases for all intervention end users. In step 4, the intervention interface was created through iterative lab testing, and we gathered training resources. In step 5, a two-stage implementation plan was devised with the recruited rehabilitation sites. In step 6, we proposed an evaluation protocol. This detailed account of the development of MOvIT+™ demonstrates how the combined use of an intervention mapping approach and participatory processes with end users can help linking evidence-based, user-centered, and pragmatic reasoning. It makes visible the complexities behind the development of Internet-based interventions, while guiding future program developers.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".