Enhancing Ultrasound Access in Rural Saskatchewan: A Mixed-Methods Study of Telerobotic Technology
Bibliographic record
Abstract
Objective: Access to health care services, including diagnostic tools, such as sonography, remains limited in rural areas and could lead to negative health outcomes. Telerobotic ultrasonography (TUS) systems, which enable remote sonography from centralized locations, offer a promising solution to this challenge. This study examined the implementation of TUS in a rural community in southern Saskatchewan. Materials and Methods: A non-sequential mixed-methods research approach was used to study this intervention. A thematic analysis was conducted of the surveys and interviews that were conducted. The work was guided by the Canadian Network for Digital Health Evaluation Framework and Khan’s Access to Care and Prevention Framework. Results: Data were collected through 25 semi-structured interviews with providers (n = 11) and patients (n = 14). This was complemented by surveys from patients (n = 44). Findings revealed that accessibility, convenience, and timeliness of TUS significantly influenced acceptance and utilization. Patients valued the technology’s ability to deliver local care, minimizing disruptions like travel to an urban center. Among providers, enhanced coordination between technical and non-technical staff and service expansion emerged as pivotal for optimizing health care delivery. Conclusion: These results underscore the importance of increasing awareness and refining the integration of TUS to improve diagnostic access for underserved communities. The possibility of implementing TUS must focus on the magnitude of use and advancement directions to provide equitable health care delivery.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".