Rehabilitation and Research Priorities in Deafblindness for the Next Decade
Bibliographic record
Abstract
Purpose Previous studies have guided the development of deafblindness rehabilitation by reporting on the priorities of researchers and of deafblind individuals; however, service and care providers may be able to bring a different and important perspective to shaping the development of this clinical field and its research. The present study aims to give them a voice in this process. Methods We conducted a qualitative survey in which 68 stakeholders in deaf-blind rehabilitation from 6 countries described their perceived research and rehabilitation priorities for the coming decade. Verbatim transcripts were analyzed using thematic analysis through open coding, creating categories, and abstraction. Results Three categories of priorities emerged: those common to rehabilitation and research (such as assistive technology, communication services, and interdisciplinarity), those unique to rehabilitation (such as augmented service provision), and those unique to research (such as recruitment databases and measurement tools). Conclusions When viewing the findings from within the context of the existing research literature, the overlap indicated that research and rehabilitation efforts are moving in a congruent direction for researchers, service providers, and persons with deafblindness. Future efforts should focus on information exchange in order to improve evidence-based rehabilitation practice.
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 | 0.095 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".