Stakeholders’ Perspectives, Needs, and Barriers to Self-Management for People With Physical Disabilities Experiencing Chronic Conditions: Focus Group Study
Bibliographic record
Abstract
BACKGROUND: While self-management programs have had significant improvements for individuals with chronic conditions, less is known about the impact of self-management programs for individuals with physical disabilities who experience chronic conditions, as no holistic self-management programs exist for this population. Similarly, there is limited knowledge of how other stakeholders, such as caregivers, health experts, and researchers, view self-management programs in the context of disability, chronic health conditions, and assistive technologies. OBJECTIVE: This study aimed to obtain insight into how stakeholders perceive self-management relating to physical disability, chronic conditions, and assistive technologies. METHODS: Nine focus groups were conducted by 2 trained facilitators using semistructured interview guides. Each guide contained questions relating to stakeholders' experiences, challenges with self-management programs, and perceptions of assistive technologies. Focus groups were audio recorded and transcribed. Thematic analysis was conducted on the focus group data. RESULTS: A total of 47 individuals participated in the focus groups. By using a constructivist grounded approach and inductive data collection, three main themes emerged from the focus groups: (1) perspectives, (2) needs, and (3) barriers of stakeholders. Stakeholders emphasized the importance of physical activity, mental health, symptom management, medication management, participant centeredness, and chronic disease and disability education. Participants viewed technology as a beneficial aide to their daily self-management and expressed their desire to have peer-to-peer support in web-based self-management programs. Additional views of technology included the ability to access individualized, educational content and connect with other individuals who experience similar health conditions or struggle with caregiving duties. CONCLUSIONS: The findings suggest that the development of any web-based self-management program should include mental health education and resources in addition to physical activity content and symptom management and be cost-effective. Beyond the inclusion of educational resources, stakeholders desired customization or patient centeredness in the program to meet the overall needs of individuals with physical disabilities and caregivers. The development of web-based self-management programs should be holistic in meeting the needs of all stakeholders. TRIAL REGISTRATION: ClinicalTrials.gov NCT05481593; https://clinicaltrials.gov/study/NCT05481593.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".