A study of human resource competencies required to implement community rehabilitation in less resourced settings
Bibliographic record
Abstract
BACKGROUND: It is estimated that over one billion persons worldwide have some form of disability. However, there is lack of knowledge and prioritisation of how to serve the needs and provide opportunities for people with disabilities. The community-based rehabilitation (CBR) guidelines, with sufficient and sustained support, can assist in providing access to rehabilitation services, especially in less resourced settings with low resources for rehabilitation. In line with strengthening the implementation of the health-related CBR guidelines, this study aimed to determine what workforce characteristics at the community level enable quality rehabilitation services, with a focus primarily on less resourced settings. METHODOLOGY: This was a two-phase review study using (1) a relevant literature review informed by realist synthesis methodology and (2) Delphi survey of the opinions of relevant stakeholders regarding the findings of the review. It focused on individuals (health professionals, lay health workers, community rehabilitation workers) providing services for persons with disabilities in less resourced settings. RESULTS: Thirty-three articles were included in this review. Three Delphi iterations with 19 participants were completed. Taken together, these produced 33 recommendations for developing health-related rehabilitation services. Several general principles for configuring the community rehabilitation workforce emerged: community-based initiatives can allow services to reach more vulnerable populations; the need for supportive and structured supervision at the facility level; core skills likely include case management, social protection, monitoring and record keeping, counselling skills and mechanisms for referral; community ownership; training in CBR matrix and advocacy; a tiered/teamwork system of service delivery; and training should take a rights-based approach, include practical components, and involve persons with disabilities in the delivery and planning. CONCLUSION: This research can contribute to implementing the WHO guidelines on the interaction between the health sector and CBR, particularly in the context of the Framework for Action for Strengthening Health Systems, in which human resources is one of six components. Realist syntheses can provide policy makers with detailed and practical information regarding complex health interventions, which may be valuable when planning and implementing programmes.
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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 | 0.051 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| 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".