Resilience practices among a broad spectrum of individuals with physical disabilities during the COVID‐19 pandemic: A qualitative photo elicitation study
Bibliographic record
Abstract
This community-based study explored resilience practices among people living with physical disabilities (i.e., stroke, spinal cord injury, and other physical disabilities) during the COVID-19 pandemic. In this photo elicitation study, during 1:1 interviews, participants shared and described photos that reflected their pandemic-related experiences. Data were analyzed thematically to identify resilience-related practices. Our analysis revealed three themes: (1) reflecting on the importance of family, friends, and community (e.g., recalling past memories and strengthening existing connections); (2) engaging in social and recreational activities (e.g., experiencing the outdoors and gardening); and (3) reframing personal contexts and social environment (e.g., adjusting to new social norms and overcoming physical barriers to navigating safely during the pandemic). The resilience that participants identified encompassed not only individual strategies but also family and community supports. Resilience can be fostered through community initiatives that support more equitable responses to health emergencies for people with disabilities.
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.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".