Navigating uncharted territory: a qualitative study of the experience of transitioning to wheelchair use among older adults and their care providers
Bibliographic record
Abstract
BACKGROUND: An increasing number of older adults are procuring a wheelchair for mobility; however, the corresponding impact on related injuries, caregiver burden, and participation restriction is concerning. To inform the development of a wheelchair training program, we pursued a clearer understanding of the experience transitioning to wheelchair use for older adult users and their care provider. METHODS: Six focus groups were conducted with older experienced wheelchair users (n = 10) and care providers (n = 4). Transcripts were analyzed using a Conventional Content approach; a coding framework enabled inductive theming and summary of the data. RESULTS: Three themes emerged from the user group: On My Own reflected both limited training and the necessity of venturing out, More Than Meets the Eye addressing barriers to use, and Interdependence between wheelchair users and the ambulatory community. Care provider responses fell into two themes: the All Encompassing impact of assumed responsibilities and Even the Best Laid Plans, where unpredictable and inaccessible environments sabotaged participation. CONCLUSIONS: The transition from ambulatory to wheelchair mobility can feel like uncharted territory. Balanced support and appropriate mentorship are fundamentally important and real-world encounters optimize independence and proficiency with skills. The impact on care providers is extensive, highlighting the importance of skills training.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".