Critical perspectives on rehabilitation education, practice and process: northern Honduras case study
Bibliographic record
Abstract
BACKGROUND: Rehabilitation services are an integral part of patient care, but in many developing countries, they are not prioritized and either unavailable or easily accessible to those who need them. Although the need for rehabilitation services is increasing in Honduras, rehabilitation workers are not included in the health care model that guides the care provided to communities, particularly in rural and remote areas. To understand the need for providing impactful rehabilitation services in disadvantaged communities, we explored the education and perception of the community relating to rehabilitation, investigated training available for rehabilitation workers, and examined the rehabilitation processes and practices in Northern Honduras from stakeholders' experiences. METHODS: We utilized a qualitative descriptive and interpretive approach grounded in case study methodology to understand rehabilitation education, process, and practice in Northern Honduras. Three rehabilitation centres were purposefully selected as the cases, and participants consisted of rehabilitation workers and managers from these centres. We collected data via interviews and focus group sessions. We analyzed the data via thematic analysis using NVivo version 12. RESULTS: In Northern Honduras, rehabilitation workers' limited training and continuing education, along with awareness about rehabilitation by community members and other health providers influence rehabilitation care. Although policies and initiatives to support people with disabilities and the broader community in need of rehabilitation exist, most policies are not applied in practice. The sustainability of rehabilitation services, which is rooted in charity, is challenged by the small range of funding opportunities strongly affecting rehabilitation care processes and clinical practices. The lack of trust and awareness from the medical profession towards rehabilitation workers sets a major barrier to referrals, interdisciplinary work, and quality of life for individuals in need of rehabilitation. CONCLUSION: This study advances knowledge of the need to increase understanding of rehabilitation care among community members and health providers, improve care processes and resources, and foster interprofessional practice, to enhance the quality of care and promote equitable care delivery, especially in rural and remote communities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".