Family networks to improve outcomes in children with intellectual and developmental disorders: a qualitative study
Bibliographic record
Abstract
BACKGROUND: There are at least 50 million children with an intellectual or developmental disorder in South Asia. The vast majority of these children have no access to any service and there are no resources to develop such services. We aimed to explore a model of care-delivery for such children, whereby volunteer family members of affected individuals could be organized and trained to form an active, empowered group within the community that, a) using a task-sharing approach, are trained by specialists to provide evidence-based interventions to their children; b) support each other, with the more experienced FaNs i.e. family networks, providing peer-supervision and training to new family members who join the group; and c) works to reduce the stigma associated with the condition. METHODS: We used qualitative methods to explore carers' perspectives about such a care-delivery model. RESULTS: The key findings of this research are that there is a huge gap between the needs of the carers and available services. Carers would welcome a volunteer-led service, and some community members would have time to volunteer. Raising community awareness in a culturally sensitive manner prior to launching such a service and linking it to the community health workers programme would increase the likelihood of success. Gender-matching would be important. It would be possible to form family networks around the more motivated volunteers, with support from local non-governmental organizations. The carers were receptive to the use of technology to assist the work of the volunteers as well as for networking. CONCLUSIONS: We conclude that family volunteers delivering evidence-based packages of care after appropriate training is a feasible system that can help reduce the treatment gap for childhood intellectual and developmental disorders in under-served populations.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 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".