The use of technology to improve health care to Saskatchewan’s First Nations communities
Bibliographic record
Abstract
BACKGROUND: Saskatchewan is a province of over one million people and over 13% are Indigenous peoples, many of whom live on reserve lands. Despite continued efforts, access to health care remains a significant challenge for these Indigenous people, especially those in the North. OBJECTIVE: To address this challenge, Saskatchewan's health care providers have been incorporating the use of technology for various health services. This paper describes various ways technology has been used in First Nations communities in Saskatchewan. METHODS: Several pilot projects between First Nations leaders and health care providers, in the communities as well as in the urban setting, have taken place over the past 10 years. Information on these pilots was supplemented with literature reviews and consultations with colleagues at the Northern Inter-Tribal Health Authority, the First Nations and Inuit Health Branch (FNIHB), Health Canada and lead physicians for services to the North. RESULTS: with greater speed, sensitivity and specificity than the conventional acid-fast bacilli (AFB) smear. Similarly, telemedicine remote communications equipment is being used for patient care across communities. Panorama is a comprehensive, integrated public health information system designed for public health professionals and is currently being introduced in 21 communities in Saskatchewan. CONCLUSION: Not only do these innovative technologies appear to improve access and enhance the quality of timely care in remote communities but they also bring comfort to patients, prevent unnecessary transportation and minimize time away from work and family. Although these technologies are not a panacea for some of the determinants of health that can affect the incidence and severity of infectious diseases in First Nations, they do appear to address some of the geographic challenges faced in providing health services in remote communities.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.046 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".