Sustainability Criteria for CBR Programmes – Two Case Studies of Provincial Programmes in Vietnam
Bibliographic record
Abstract
Purpose: This paper aims to explore the conditions needed for sustainable community based rehabilitation (CBR) programmes for persons with disabilities in Vietnam, and to identify the conditions and opportunities missing at present for the implementation of such programmes. Method: Two CBR programmes in Vietnam, one medical based and one comprehensive (medical, educational, livelihood, social and empowerment), were evaluated for requirements and the current situation. Four factors were taken into account - human resources, organisational setting, social and political environment, and financing. Data were collected through interviews with programme managers and focus groups with stakeholders from provincial, district and communal levels, and with persons with disabilities. Persons with disabilities also completed a questionnaire to evaluate their satisfaction with the programme and their involvement in it. Results: The conditions needed for a sustainable CBR programme were identified: availability of human resources, training, monitoring and evaluation, collaboration, commitment and financing. The conditions missing at present were: a stable pool of human resources (in both programmes), collaboration between sectors and with local authorities (in the medical programme), and knowledge about how to maintain financing (in both programmes). Persons with disabilities were more satisfied with their involvement in the comprehensive programme than in the medical programme. Stakeholders proposed opportunities to increase sustainability; highest priority was given to a collaboration plan (comprehensive CBR programme) and to involvement of other sectors in the CBR Steering Committee (medical CBR programme). Conclusions: Few differences were found in conditions needed for sustainability of the medical and comprehensive programmes. The existence of disabled persons’ organisations (DPOs) seemed to be associated with the level of satisfaction persons with disabilities felt with their involvement in the programme. Limitations: The People’s Committee was not involved in this research, although their input was perceived to be important. Generalisation of the results of this study should be done with caution because health system structures and organisational levels of CBR differ.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".