Collaborative engagement of Hispanic communities in the planning, conducting, and dissemination of assistive technology research
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
Abstract Introduction: Community engagement (CE) is critical for research on the adoption and use of assistive technology (AT) in many populations living in resource-limited environments. Few studies have described the process that was used for engaging communities in AT research, particularly within low-income communities of older Hispanic with disabilities where limited access, culture, and mistrust must be navigated. We aimed to identify effective practices to enhance CE of low-income Hispanic communities in AT research. Methods: The community stakeholders included community-based organizations, the community healthcare clinic, the local AT project, and residents of the Caño Martín Peña Community in San Juan, Puerto Rico. The CE procedures and activities during the Planning the Study Phase comprised working group meetings with stakeholders to cocreate the funding proposal for the study and address the reviewers’ critiques. During the Conducting the Study Phase , we convened a Community Advisory Board to assist in the implementation of the study. During the Disseminating the Study Results Phase , we developed and implemented plans to disseminate the research results. Results: We identified seven distinct practices to enhance CE in AT research with Hispanic communities: (1) early and continuous input ; (2) building trusting and warm relationships through personal connections ; (3) establishing and maintaining presence in the community ; (4) power sharing ; (5) shared language ; (6) ongoing mentorship and support to community members ; and (7) adapting to the changing needs of the community . Conclusion: Greater attention to CE practices may improve the effectiveness and sustainability of AT research with low-income communities.
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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 | 0.013 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| 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