Navigating a way forward: using focused ethnography and community readiness to study disability issues in Ladakh, India
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: This article offers a discussion about the use of focused ethnography and the community readiness model to study disability at the community level in cross-cultural or international settings. It describes lessons learned when applying these methods to inform community-based disability programming in remote, rural villages in Ladakh, India. METHODS: Data were collected from 30 persons with disabilities, family members and community leaders in four remote villages using interviews and participant observation. All interviews were analysed qualitatively using a mix of inductive and deductive techniques. Community readiness interviews were scored using anchored rating scales to determine level of 'readiness' to take action on meeting the needs of persons with disabilities. Following the initial assessment, community workshops were used to disseminate results and facilitate local engagement in planning and intervention. RESULTS: There were minor challenges and significant benefit in the application of these two approaches in Ladakh; outcomes included: a known level of community readiness that can be used to improve targeting of appropriate community-based intervention and assess change over time, identification of salient needs, barriers and facilitators for persons with disabilities and their families; and community-level engagement during and following the research. CONCLUSIONS: Research models with participatory components like focused ethnography and community readiness hold significant promise for planning and evaluating community-based disability programmes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it