Community-based rehabilitation/community based inclusive development functioning during the COVID-19 pandemic: A secondary analysis of qualitative data
Bibliographic record
Abstract
INTRODUCTION: The coronavirus (COVID-19) became a global pandemic in March 2020 and impacted nations worldwide not only because of the disease but also because the containment measures-imposed created ripple effects for the populations in each country. The COVID-19 pandemic disproportionately affected vulnerable groups, such as persons with disabilities. This study aimed to understand the impact of COVID-19 on the function of Community-Based Rehabilitation (CBR)/Community-Based Inclusive Development (CBID) across nations and for their target communities-persons with disabilities. The current article also described some measures CBR/CBID programs took in light of service closure to facilitate access to needed services for persons with disabilities. METHODS: We conducted a secondary analysis of qualitative data to understand the impact of COVID-19 on the functioning of CBR/CBID programs and their target communities. The original qualitative data were collected through online dialogues among CBR/CBID partners across five regions of the world, facilitated for understanding of their practices on five other topics. FINDINGS: COVID-19 significantly impacted the function of CBR/CBID programs across the world. Many services were halted due to public health measures, such as maintaining social distancing or lockdowns. The COVID-19 pandemic also had a negative impact on access to health, education and livelihood support for persons with disabilities. Additionally, many people with disabilities did not have access to COVID-19 related information and services like vaccines. However, we found that technology played a significant role in revitalizing CBR/CBID programs during COVID-19. CBR/CBID service providers across five regions used online platforms to disseminate information about COVID-19. Professionals also used technology to provide rehabilitation and educational services to people with disabilities through online platforms. CONCLUSION: Our findings suggest that technology can play a vital role in continuing many services (e.g., CBR/CBID) that cannot be offered in person during crises like COVID-19. However, it is important to remember that technology may not be accessible to many individuals with disabilities, specifically those who reside in rural areas and who experience adverse situations like financial constraints. Additionally, many persons with disabilities may not have the necessary knowledge and skills to use technology. CBR personnel must consider that before adopting technology to provide services under CBR programs.
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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 | 0.032 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".