Bibliographic record
Abstract
Background The design and development of digital health platforms do not routinely incorporate psychosocial aspects and theory that may increase engagement and motivation for using the digital solution. However, emerging evidence suggests that when integrating both theory and psychosocial aspects in the design process, the resulting digital health platforms may be superior in engaging and motivating patients to use them. Objective The objective of our study was to give an overview of how and why integrating psychosocial aspects in digital solutions may be essential to engaging and motivating patients to use digital solutions. Methods We conducted a brief narrative review of studies integrating psychosocial aspects and theory in the design and development of digital solutions for telerehabilitation. Results In the studies identified, self-determination theory, incorporating the patient’s perspective, and using behavioral or psychological interventions on digital platforms are among some of the theoretical and psychosocial aspects that have already been used to design and develop digital solutions for telerehabilitation. Conclusions Based on the literature, it is argued that integrating psychosocial aspects and theory in the design and development of digital solutions for telerehabilitation may result in platforms of increased value to both health providers and patients.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".