Clinicians’ Opinions, Suggestions, and Concerns Using Social Robots in Psychological Practice
Bibliographic record
Abstract
In the past decade, technology-assisted interventions for mental health have been growing. To appropriately design technologies to support mental health, such as social robots, a crucial step involves understanding the perspectives, needs, and concerns of clinicians who deliver mental health services. Our research aims to understand mental health clinicians’ perspectives on the use of social robots within various aspects of clinical practice. We conducted an online study involving 49 clinicians registered with provincial psychological regulatory bodies within Canada. The participants responded to questionnaires regarding their general views on technology/social robots, rated the degree of advantage/disadvantage of using social robots in different components of clinical practice (Screening, Diagnosis, Intervention, Administration, and Maintenance), and provided open-ended responses regarding potential applications for social robots in clinical activities. Two experimental conditions were designed, in which the participants were either introduced to social robots through a short video (exposure condition) or did not receive such an introduction (non-exposure condition). The results of the quantitative analysis indicated that exposing clinicians to more information about social robots did not yield significant rating differences, but their initial familiarity with social robots was positively associated with their perceptions. On average, clinicians’ ratings were neutrally valenced (around the midline of positive and negative), indicating, as a group, ambivalence about using social robots. They noted more advantages for social robots to support administrative roles and maintenance roles (maintaining clinical gains post-intervention). Qualitative results, i.e., thematic analysis, identified 93 codes, 18 sub-themes, and two overarching themes: perceived advantages and disadvantages. Noteworthy applications of social robots included “intake”, “administering clinical services”, “administered tasks”, and “assistant to clinicians”, while top disadvantages were “unsuitability”, “detrimental to therapeutic relationship”, and “limitations of robot relative to human capabilities”. Clinicians identified social robots as valuable tools for standardized, structured, and non-judgmental therapeutic care; however, concerns about their impact on the therapeutic relationship and limitations compared to human capabilities emphasize the importance of careful integration. The proposed guidelines aim to navigate these challenges and offer a framework for the nuanced incorporation of social robots into mental healthcare practices.
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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 | 0.026 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".