Real-world evidence for primary care: A primer on observational research
Bibliographic record
Abstract
Observational studies offer a non-experimental and minimally disruptive approach for generating real-world evidence, making them particularly valuable for informing clinical practice, research and health system strengthening - especially in primary care. This article, part of the African Journal of Primary Health Care Family Medicine (PHCFM) methods series, introduces key observational study designs including cross-sectional, cohort and (nested) case-control studies and discusses their application in doctoral-level research. Drawing on historical and contemporary examples, we examine methodological considerations, ethical issues and modern analytical strategies essential for the careful planning and execution of observational research. By integrating conceptual frameworks and causal inference methods, this primer aims to equip researchers at different career stages with a foundational understanding of how to choose and implement observational designs that are both methodologically robust and relevant to primary care contexts.
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 | 0.234 | 0.264 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.004 | 0.038 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.016 | 0.029 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".