Evaluating use and outcomes of mobility technology: A multiple stakeholder analysis
Bibliographic record
Abstract
Purpose: This qualitative, multi-site study compared and contrasted the outcomes of mobility technology (MT) and the factors influencing these outcomes from the perspective of MT users, caregivers, and professionals involved in MT service delivery.Method: Qualitative focus groups were held in the USA and Canada with multiple stakeholder groups (consumer: n = 45, caregiver: n = 10, service provider: n = 10). Data were analyzed thematically.Results: MT outcomes were conceptualized by participants as a match between expectations for MT and the actual outcomes experienced. Several factors influenced the match including a) MT features, b) environmental factors (e.g. built/physical environment, societal context of acceptance, MT delivery systems/policies), and c) the ability to self-manage the interaction across person, technology and environment, which involved constant negotiation and strategizing. Stakeholders identified MT outcomes that corresponded to ICF levels including body structure and function, activity, and participation across environments; however, varied on their importance and influence on MT impact.Conclusions: The conceptual fit model and factors related to self-management of MT represent new knowledge and provide a framework for stakeholder-based evaluation of MT outcomes. Implications for MT assessment, service delivery, outcomes research, and interventions are discussed.Implications for RehabilitationThere is a need for research on mobility technology (MT) such as canes, walkers and wheelchairs that documents the experiences of people with disabilities using MT. This qualitative, multi-site study compared and contrasted the outcomes of MT and the factors influencing these outcomes from the perspective of MT users, caregivers, and professionals involved in MT service delivery. Results from this research inform our understanding of MT use, assessment and outcomes.
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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 | 0.037 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".