El Derecho Romano después de Europa: la historia jurídica para la formación del jurista y ciudadano europeo
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
<b><i>Objective:</i></b> The aim of this experiential review is to explore the state of the art of the literature regarding the evaluation tools available for assessment of patient motivation and satisfaction during technology-assisted rehabilitation (robot rehabilitation, virtual reality rehabilitation, and serious games rehabilitation). <b><i>Materials and Methods:</i></b> A systematic search of the peer-reviewed literature published from January 1990 to August 2019 was conducted. The protocol for this review was registered in PROSPERO and carried out in accordance with the PRISMA recommendations. <b><i>Results:</i></b> The search of PubMed, PsycINFO, Scopus, and Web of Science databases identified a total of 333 records. After adjusting for duplicates and other inclusion criteria, 69 studies were selected for inclusion in the review. We found that authors used a wide range of dedicated questionnaires and, in about 50% of studies, a few validated tools to assess motivation and satisfaction during technology-assisted rehabilitation. The instruments most used were the Intrinsic Motivation Inventory (IMI), Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST 2.0), and the Usefulness, Satisfaction, and Ease of use (USE) scale. Motivation and satisfaction were generally portrayed as multidimensional concepts; overall, 29 domains were assessed by 9 different tools. <b><i>Conclusion:</i></b> The tools used in the current literature to assess patient motivation and satisfaction during technology-assisted rehabilitation are quite variegated, but we would recommend use of the IMI and USE questionnaires based on their widespread diffusion. However, the choice of domains explored and number of items calls for harmonization. Ideally, this should be a joint task for the whole scientific community.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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