Social Acceptance of Smart Glasses in Health Care: Model Evaluation Study of Anticipated Adoption and Social Interaction
Bibliographic record
Abstract
BACKGROUND: Despite the growing interest in smart glasses, it is striking that they are not widespread among health care professionals. Previous research has identified issues related to social interactions involving the use of smart glasses in public settings, which may differ from those associated with their application in health care contexts. OBJECTIVE: Assuming that smart glasses mediate contact between the health care provider and patient, the objectives of this research are two-fold: (1) to develop an instrument that combines the adoption and mediation perspectives, and (2) to gain insights into how the intention to use is influenced through aspects of adoption and social interaction. METHODS: A questionnaire was administered to a target audience of health care professionals (N=450), with recruitment via MTurk. The sample primarily included male participants from the United States, with the majority aged 42 years or younger. Although a large portion of respondents were medical doctors, the sample also included nurses and other health care professionals. Data were analyzed by structural equation modeling. RESULTS: Regarding the aim of developing an instrument combining adoption and social interaction, the internal consistency was above the aspirational level (α>.70) for the instrument. Furthermore, regarding the second objective involving gaining insights into the influential constructs of the anticipated intention to use, the following results were highlighted: in testing the conceptual model, the measurement model generated a good fit and the respecified structural model also generated a good fit. The tested hypotheses confirmed that social interaction constructs could explain a higher variance of users' anticipated intention to use. Perceived social isolation and decreased attentional allocation did not have a significant effect on attitude. Furthermore, the intention to use smart glasses despite nonacceptance of smart glasses by the patient significantly influenced the anticipated intention to use. In summary, constructs that focus on social interaction could contribute to better explanation and prediction of the expected adoption of smart glasses in health care. CONCLUSIONS: The empirical findings of this study provide new insights into how the mediation perspective can increase the explained variance compared to existing knowledge about adoption. Against expectations based on previous literature and despite the social issues raised earlier, these social aspects do play important roles for health care professionals but are ultimately not decisive for the intention to use. As a result, there are fewer threats to the adoption of smart glasses from the perspective of health care professionals than might be expected based on the previous literature. Therefore, the use of smart glasses can still be considered as an innovative way of working in health care.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".