THE TREAT SCALE: A REFLEXIVE TOOL FOR TRANSDISCIPLINARY WORKING IN AGING AND TECHNOLOGY RESEARCH
Bibliographic record
Abstract
Adopting a transdisciplinary approach to the development of technologies to support older adults and their care partners is crucial to bridging research with policy and practice. Despite increased popularity of this approach, research evaluating transdisciplinary processes and outcomes remains limited due to an absence of evaluative tools. This presentation describes the development and validation of a new instrument: TREAT (Transdisciplinary Research Effectiveness in Aging and Technology) scale. Content areas were established through a scoping review resulting in four key themes: collaborative working practices, knowledge mobilization and exchange, integration and co-creation of knowledge, and action-oriented research. Subscale items were developed according to each theme. The overall structure, phrasing, and content validity of the TREAT were evaluated via consultation workshops with key stakeholders across Canada. Results suggest that the TREAT scale has significant potential for evaluating and improving transdisciplinary processes and outcomes for the field of aging and technology.
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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 | 0.089 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".