Using School-Based Teleconsultation Services to Make Community Health Services Accessible in Semirural Settings of Pakistan: Sequential Explanatory Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: In Pakistan's remote areas, quality health care and experienced professionals are scarce. Telehealth can bridge this gap by offering innovative services like teleconsultations. Schools can serve as effective platforms for introducing these services, significantly improving health service access in semirural communities. OBJECTIVE: This study aims to explore the feasibility of introducing school-based teleconsultation services (TCS) to strengthen community health in a semirural area of Karachi, Pakistan. METHODS: This study used a mixed methods design. A total of 393 students were enrolled for the quantitative component, while 35 parents, teachers, and community stakeholders participated in the qualitative arm (focused group discussion). Proportional computation for the quantitative data was done using SPSS (version 24; IBM Corp), while qualitative data underwent thematic analysis. RESULTS: A total of 1046 successful teleconsultations were provided for 393 students over 28 months. The demographic data showed that the mean age of the students availing TCS was 9.24 (SD 3.25) years, with the majority being males (59.3%, 233/393). Only 1.24% (13/1046) of cases required referrals. The qualitative analysis yielded three themes: (1) transformation of the health care experience, (2) escalating demands for teleconsultation, and (3) the psychological aspect of care. CONCLUSIONS: This study demonstrated the efficacy of integrating TCS in a semiurban school in Karachi to address health care accessibility gaps. Implementing TCS through the school platform improved the overall health status of school children while reducing school absences and financial burdens on families. The study highlighted TCS's cost-effectiveness, time efficiency, and quality, with community support for 24/7 availability, expansion to adults, and a reimbursement model. School health nurse-led TCS offers a scalable solution to health care challenges, enhancing health outcomes for school-going children in Pakistan and globally, particularly in low- and middle-income countries, where accessibility is a major issue.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".