Defining and evaluating transdisciplinary research: implications for aging and technology
Bibliographic record
Abstract
Purpose: Transdisciplinary research has the potential to enhance the real-world impact of the field of aging and technology. This is a context-driven and problem-focused approach to knowledge production that involves collaboration across scientific disciplines and academic and nonacademic sectors. To sustain broader implementation of this approach, a scoping review was conducted on the impact of this approach on research processes, outcomes and uptake.Materials and Methods: A systematic search was conducted of aging, health/medicine, and technology literatures indexed in three electronic data bases (Medline/OVID, EBSCO, ProQuest) from 1 January 2005 to 31 December 2015. Search terms included three themes: (1) transdisciplinarity; (2) research outcomes and (3) social change.Results: Twenty articles met the inclusion criteria. We found that a transdisciplinary approach to research enhances integration of diverse knowledge, scientific and extra-scientific outcomes, capacity to engage in translational research and the uptake of research knowledge. We also identified a number of facilitators and barriers to successful implementation of this approach. No articles evaluating transdisciplinary research specifically in the context of aging and technology were found.Conclusions: Adoption of transdisciplinary research in aging and technology may foster greater uptake of technological innovation in the real-world by supporting integration of diverse knowledge and enhancing engagement of experiential and nonacademic stakeholders in the research and development process. However, supporting successful implementation of this approach requires investment of personal and structural resources. More research is needed to better understand the evidence base on the adoption of this approach in aging and technology projects.IMPLICATIONS FOR REHABILITATIONTransdisciplinary research is context-driven and problem-focused and involves collaboration between academic and non-academic sectors.A transdisciplinary approach can enhance knowledge integration, scientific productivity and capacity and public involvement in research.Future research is needed to determine the effectiveness of transdisciplinarity for optimizing the development and uptake of assistive technologies.
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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 | 0.617 | 0.752 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.028 | 0.035 |
| Science and technology studies | 0.012 | 0.047 |
| Scholarly communication | 0.050 | 0.060 |
| Open science | 0.009 | 0.032 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".