Clinicians’ Assessment of Mobile Monitoring: A Comparative Study in Japan and Spain
Bibliographic record
Abstract
BACKGROUND: The gradual but steady shift toward telemedicine during the past decades is a clear response to important health problems that most industrialized countries have been facing. The growing elderly population and changing dietary habits have led to an increase in people with chronic diseases and overall health care expenditures. As more consumers use their mobile device as their preferred information and communication technology (ICT) device, mobile health monitoring has been receiving increasing attention in recent years. OBJECTIVE: This study examines clinicians' perception of factors determining mobile health monitoring acceptance in Japan and Spain. The study proposes a causal model consisting of innovation seeking, new ICT attributes (perceived value, time-place flexibility, and compatibility), and usage intention. In addition, cross-country differences are posited for the hypothesized relationships among the proposed constructs. METHODS: A questionnaire survey was performed to test our research model and hypotheses. The sample consisted of clinicians from various medical specialties. In total, 471 and 497 usable responses were obtained in Japan and Spain, respectively. RESULTS: In both countries, the collected data fit the model well with all the hypothesized paths among the constructs being supported. Furthermore, the moderating effects of psychic distance were observed in most of the paths. CONCLUSIONS: Our study demonstrates the importance of new ICT attributes, namely perceived value, time-place flexibility, and compatibility, in the clinicians' adoption of mobile health monitoring. In particular, our results clearly indicated that perceived medical value and ubiquitous nature of the tool are the two main benefits clinicians are likely to perceive (and appreciate) in both countries. This tendency will be stronger for those with a greater propensity to seek innovation in ICT. In terms of cross-country comparison, the strength of the path from innovation seeking to perceived value was greater in Japan than in Spain. Since the number of clinicians per 10,000 residents is substantially fewer in Japan compared with Spain, clinicians with a greater propensity to seek innovation in ICT may have perceived greater value in using mobile health monitoring to improve remote patient care.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".