Intervention mapping of a mobility outcomes monitoring system for geriatric patients
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Bibliographic record
Abstract
Purpose Evidence suggests that providing training and follow-up for older adults with mobility limitations after mobility device provision can improve adherence to device use and function (Best et al., 2016), but there is a lack of systematic and coordinated services.To address this gap, a theoretically informed digital intervention called MOvIT+ was designed to provide remote monitoring and support for older adults and their caregivers (Auger et al., 2022).This paper explicitly describes how, using intervention mapping (Eldredge et al., 2016) and a novel approach to a collective decision-making method, the features of the intervention were linked with intervention users' needs and program outcomes for older adults using mobility assistive technology, their caregivers, and health professionals.Method A user-centered design grounded in a 6-step intervention mapping approach (Eldredge et al., 2016).Older adults and their caregivers were involved in the co-design process to ensure the intervention addressed their needs.Results and Discussion In step 1, a logic model was created, a governance structure for the project was established, and 66 potential functionalities were identified.For step 2, a novel modified TRIAGE approach was used to prioritize 36 intervention features (Table 1).Step 3 consisted of establishing a theoretical framework and creating 28 use-case scenarios for all intervention users (assistive technology users, caregivers, clinicians, managers, and research staff).In step 4, the digital infrastructure for the monitoring intervention was constructed and more than 130 training resources were gathered to address an array of potential problems with the use of assistive technologies.In step 5, an iterative implementation plan was devised with the steering committee and improved continuously by the participating sites' feedback.Lastly, for step 6, an evaluation protocol was finalized.In conclusion, by utilizing an intervention mapping approach, the complexities of a multi-component digital intervention were manageable due to the stepwise approach and the resulting logic model.The systematic and collaborative approach used ensures the intervention features will meet targeted objectives.Furthermore, the explicit links between intervention features and behavior changes will assist evaluation in future studies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it