Remote Consulting in Primary Health Care in Low- and Middle-Income Countries: Feasibility Study of an Online Training Program to Support Care Delivery During the COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND: Despite acceleration of remote consulting throughout the COVID-19 pandemic, many health care professionals are practicing without training to offer teleconsultation to their patients. This is especially challenging in resource-poor countries, where the telephone has not previously been widely used for health care. OBJECTIVE: As the COVID-19 pandemic dawned, we designed a modular online training program for REmote Consulting in primary Health care (REaCH). To optimize upscaling of knowledge and skills, we employed a train-the-trainer approach, training health workers (tier 1) to cascade the training to others (tier 2) in their locality. We aimed to determine whether REaCH training was acceptable and feasible to health workers in rural Tanzania to support their health care delivery during the pandemic. METHODS: We developed and pretested the REaCH training program in July 2020 and created 8 key modules. The program was then taught remotely via Moodle and WhatsApp (Meta Platforms) to 12 tier 1 trainees and cascaded to 63 tier 2 trainees working in Tanzania's rural Ulanga District (August-September 2020). We evaluated the program using a survey (informed by Kirkpatrick's model of evaluation) to capture trainee satisfaction with REaCH, the knowledge gained, and perceived behavior change; qualitative interviews to explore training experiences and views of remote consulting; and documentary analysis of emails, WhatsApp texts, and training reports generated through the program. Quantitative data were analyzed using descriptive statistics. Qualitative data were analyzed thematically. Findings were triangulated and integrated during interpretation. RESULTS: Of the 12 tier 1 trainees enrolled in the program, all completed the training; however, 2 (17%) encountered internet difficulties and failed to complete the evaluation. In addition, 1 (8%) opted out of the cascading process. Of the 63 tier 2 trainees, 61 (97%) completed the cascaded training. Of the 10 (83%) tier 1 trainees who completed the survey, 9 (90%) would recommend the program to others, reported receiving relevant skills and applying their learning to their daily work, demonstrating satisfaction, learning, and perceived behavior change. In qualitative interviews, tier 1 and 2 trainees identified several barriers to implementation of remote consulting, including lacking digital infrastructure, few resources, inflexible billing and record-keeping systems, and limited community awareness. The costs of data or airtime emerged as the greatest immediate barrier to supporting both the upscaling of REaCH training and subsequently the delivery of safe and trustworthy remote health care. CONCLUSIONS: The REaCH training program is feasible, acceptable, and effective in changing trainees' behavior. However, government and organizational support is required to facilitate the expansion of the program and remote consulting in Tanzania and other low-resource settings.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".