Robotics in occupational therapy: Protocol for a scoping review v1
Bibliographic record
Abstract
Introduction: Although the utilization of social robots to support interventions on occupations holds promise due to the shortage of human resources, no synthesis of knowledge on this topic has beenconducted in the field of occupational therapy. Objective: Synthesize knowledge of occupational therapy interventions using robotics to foster occupations published since 2004. Inclusion/ExclusionCriteria: Based on the following inclusion and exclusion criteria, studies were selected if: (1) original empirical research published in English or French in a peer-review scientific journal, (2) involved a robot as previously defined and (3) reported an intervention directly on occupations that had been carried out within occupational therapy practice and involving an OT. As exoskeletons, prothesis and upper limb training robots are mainly used to exercise (training with repetitive motions) and not specifically to support occupations, these robots were excluded. Also, only articles were included. Method: To synthesize this knowledge, the methodological framework for scoping studies (Arksey & O’Malley, 2005; Levac, Colquhoun & O'Brien, 2010) and PRISMA standards (Moher, Liberati, Tetzlaff, Altman & PRISMA Group, 2009) will be followed. The search will be conducted across seven databases (PUBMED, SocIndex, CINAHL, MedLine, AgeLine, Abstracts in Gerontology, and OTDBase) using keywords related to occupational therapy and robotics, with the search period ending on April 15st 2025. Eligibility screening (title/abstract and full-text) and data extraction will be performed by at least two independent reviewers, and any conflicts will be resolved by the team.
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 | 0.056 | 0.062 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.016 | 0.011 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.105 | 0.018 |
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".