Lessons learned on data collection for a digital health intervention—insights and challenges from Nigeria
Bibliographic record
Abstract
Objectives: This article delves into the challenges of medical data collection during the COVID-19 pandemic in developing countries, using Nigeria as a case study. It emphasizes how data collection impacts research quality, reliability, and validity. Methods: Qualitative research utilizing purposive sampling was employed to explore experiences in designing a diagnostic tool for febrile diseases in Nigeria. A questionnaire with selectable and open-ended questions was utilized for data collection, and 23 respondents participated. Results: Among 74 potential participants, 23 valid responses were gathered, revealing significant themes related to experiences and challenges in medical data collection. A multidisciplinary team approach proved beneficial, fostering collaboration, enhancing knowledge, and promoting positive experiences. Despite challenges with paper questionnaires, most participants preferred them for ease of use. Connectivity issues hindered timely data uploading and disrupted virtual meetings. Conclusion: Innovative and flexible strategies, such as a blended data collection approach and well-coordinated teams, were vital in overcoming challenges. Electronic data collection tools, reminders, and effective communication played key roles, leading to positive outcomes. This study provides valuable insights for researchers and practitioners involved in data collection, particularly in developing countries like Nigeria.
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 | 0.046 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".