A Holistic Framework for Assisting Decision Makers of Healthcare Facilities to Assess Telemedicine Applications in Saudi Arabia
Bibliographic record
Abstract
This paper outlines some of the challenges that currently face healthcare systems in Kingdom of Saudi Arabia (KSA). Increasing and continuing demand for healthcare services is aggravated by a critical shortage of health human resources (HHR) and healthcare facilities (HCFs) especially in rural areas. In 2013, 17.8% of the population lived in rural and remote areas with a huge disparity in HCFs distribution, and 76% of physicians and 44.7% of nurses are non-Saudis. Current studies have shown the potential of telemedicine to alleviate these challenges. The use of telemedicine has been adopted and the telemedicine roadmap has been developed by the Ministry of Health (MOH) in KSA in collaboration with Canada Health Infoway (Infoway). This roadmap has identified many barriers and challenges likely to face the implementation of telemedicine in KSA. This paper describes a holistic framework to address these challenges and to assess telemedicine applications in order to assist decision makers of HCFs in KSA. The proposed framework is developed in collaboration with the National eHealth Strategy and Change Management Office in the Ministry of Health (MOH) and Prince Mohammad Medical City (PMMC) in KSA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| 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".