Navigating a way forward: using focused ethnography and community readiness to study disability issues in Ladakh, India
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".