Positive and negative experiences of caregivers helping power wheelchair users: a mixed-method study
Bibliographic record
Abstract
PURPOSE: This study aimed to better understand how the powered wheelchair (PWC) impacts the experiences of family caregivers of PWC users, and explore the strategies and resources used by caregivers to cope with their role. MATERIALS AND METHODS: This mixed-methods study was part of a larger cross-sectional research study conducted in four Canadian cities. Twenty-three family caregivers of PWC users, who provided at least 2 h of support per week, completed the Power Mobility Caregiver Assistive Technology Outcome (PM-CATOM), an 18-item measure assessing PWC-related and overall burden experienced by family caregivers. We also conducted semi-structured interviews and analysed them using inductive content analysis. RESULTS: From the quantitative PM-CATOM results, the caregivers perceived low level of burden for the wheelchair-related items, (Median:4.5; Range 3 to 5). Most perceived burden when physically helping the wheelchair user and when providing verbal hints. In terms of overall help, the caregivers experienced some level of burden (Median 3.5: Range 3 to 5). Most caregivers identified burden associated with the limitation to their recreational and/or leisure activities (52.2%) and feeling that they have more to do than they can handle. We identified 3 themes in the interviews: the burden experiences of caring for PWC users, the positive experiences of caregiving, and the coping strategies and resources used by caregivers of PWC users. CONCLUSION: Our study showed that understanding the experiences of caregivers of AT users is central as they are directly and indirectly impacted by the PWC in their lives and caregiving roles.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".