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Record W4403362879 · doi:10.4017/gt.2024.23.s.1066.opp

Intervention mapping of a mobility outcomes monitoring system for geriatric patients

2024· article· en· W4403362879 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueGerontechnology · 2024
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsNOSM University
FundersUniversité de Montréal
KeywordsIntervention (counseling)Geriatric careGerontologyMedicinePhysical medicine and rehabilitationPsychologyNursing

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.301
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it