Finding the Best Way to Deliver Online Educational Content in Low-Resource Settings: Qualitative Survey Study
Bibliographic record
Abstract
BACKGROUND: The reach of internet and mobile phone coverage has grown rapidly in low- and middle-income countries (LMICs). The potential for sharing knowledge with health care workers in low-resource settings to improve working practice is real, but barriers exist that limit access to online information. Burns affect more than 11 million people each year, but health care workers in low-resource settings receive little or no training in treating burn patients. Interburns' training programs are tailor-made to improve the quality of burn care in Asia, Africa, and the Middle East; the challenge is to understand the best way of delivering these resources digitally toward improved treatment and care of burn patients. OBJECTIVE: The aim of the study, funded by the National Institute for Health Research (NIHR), was to understand issues and barriers that affect health care worker access to online learning in low-resource settings in order to broaden access to Interburns' training materials and improve burn-patient care. METHODS: A total of 546 participants of Interburns' Essential Burn Care (EBC) course held in Bangladesh, Nepal, Ethiopia, and the West Bank, the occupied Palestinian Territories, between January 2016 and June 2018 were sent an online survey. EBC participants represent the wide range of health care professionals involved with the burn-injured patient. A literature review was carried out as well as research into online platforms. RESULTS: A total of 207 of 546 (37.9%) participants of the EBC course did not provide an email address. Of the 339 email addresses provided, 81 (23.9%) "bounced" back. Surgeons and doctors were more likely to provide an email address than nurses, intern doctors, or auxiliary health care workers. A total of 258 participants received the survey and 70 responded, giving a response rate of 27.1%. Poor internet connection, lack of time, and limited access to computers were the main reasons for not engaging with online learning, along with lack of relevant materials. Computers were seen as more useful for holding information, while mobile phones were better for communicating and sharing knowledge. Health care workers in LMICs use mobile phones professionally on a daily basis. A total of 80% (56/70) felt that educational content on burns should be available through mobile apps. CONCLUSIONS: Health care workers in low-resource settings face a variety of barriers to accessing educational content online. The reliance on email for sign-up to learning management systems is a significant barrier. Materials need to be relevant, localized, and easy to consume offline if necessary, to avoid costs of mobile phone data. Smartphones are increasingly used professionally every day for communication and searching for information, pointing toward the need for tailored educational content to be more available through mobile- and web-based apps.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".