The promise of telemedicine in Pakistan: A systematic review
Bibliographic record
Abstract
BACKGROUND: Telemedicine offers the possibility of provision of medical assistance to remote patients, and it has great potential in developing countries like Pakistan. Telemedicine solves logistical barriers, gives support to weak health systems, and helps to establish worldwide networks of healthcare professionals. Because of the high implementation costs, it is not possible yet to adopt telehealth systems for low- and middle-income nations. OBJECTIVE: To present a revision of region-based telemedical services in Pakistan. METHODS: Libraries such as PubMed (Medline), CINAHL (Cumulative Index to Nursing and Allied Health Literature), Scopus (EMBASE), and Google Scholar were used for document search. Newcastle-Ottawa Scale (NOS) was adopted to conduct study quality. Many of the studies (n-8) included in the review were of high quality as assessed through the Newcastle-Ottawa scale. Selected study characteristics were further analyzed based on different parameters such as publication year, sample size, study design, methods, motivation, and outcomes. RESULTS: Search produced 955 articles and 11 items were ultimately selected to conduct the review. These studies were further characterized as region-based telemedicine implementation. Out of 11, eight studies were conducted in the urban region and three studies were conducted in the rural areas of Pakistan. Many studies produced evidence on telehealth interventions by smartphone services such as SMS, apps, and web-based telemedicine. CONCLUSIONS: Telehealth interventions such as mHealth, eHealth, telemedicine, and telepharmacy in Pakistan were introduced starting from the last two decades. For obtaining the full benefits of these technologies, it is necessary that they but certainly need to become an integral part of Pakistan's current health infrastructure.
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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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".