What Factors Influence the Production of Orthopaedic Research in East Africa? A Qualitative Analysis of Interviews
Bibliographic record
Abstract
BACKGROUND: Research addressing the burden of musculoskeletal disease in low- and middle-income countries does not reflect the magnitude of the epidemic in these countries as only 9% of the world's biomedical resources are devoted to addressing problems that affect the health of 90% of the world's population. Little is known regarding the barriers to and drivers of orthopaedic surgery research in such resource-poor settings, the knowledge of which would help direct specific interventions for increasing research capacity and help surgeons from high-income countries support the efforts of our colleagues in low- and middle-income countries. PURPOSE: We sought to identify through surveying academic orthopaedic surgeons in East Africa: (1) barriers impeding research, (2) factors that support or drive research, and (3) factors that were identified by some surgeons as barriers and others as drivers (what we term barrier-driver overlap) as they considered the production of clinical research in resource-poor environments. MATERIALS: Semistructured interviews were conducted with 21 orthopaedic surgeon faculty members at four academic medical centers in Ethiopia, Kenya, Tanzania, and Uganda. Qualitative content analysis of the interviews was conducted using methods based in grounded theory. Grounded theory begins with qualitative data, such as interview transcripts, and analyzes the data for repeated ideas or concepts which then are coded and grouped into categories which allow for identification of subjects or problems that may not have been apparent previously to the interviewer. RESULTS: We identified and quantified 19 barriers to and 21 drivers of orthopaedic surgery research (mentioned n = 1688 and n = 1729, respectively). Resource, research process, and institutional domains were identified to categorize the barriers (n = 7, n = 5, n = 7, respectively) and drivers (n = 7, n = 8, n = 6, respectively). Resource barriers (46%) were discussed more often by interview subjects compared with the research process (26%) and institutional barriers (28%). Drivers of research discussed at least once were proportionally similar across the three domains. Some themes such as research ethics boards, technology, and literature access occurred with similar frequency as barriers to and drivers of orthopaedic surgery research. CONCLUSIONS: The barriers we identified most often among East African academic orthopaedic faculty members focused on resources to accomplish research, followed by institutional barriers, and method or process barriers. Drivers to be fostered included a desire to effect change, collaboration with colleagues, and mentorship opportunities. The identified barriers and drivers of research in East Africa provide a targeted framework for interventions and collaborations with surgeons and organizations from high-resource settings looking to be involved in global health.
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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 | 0.065 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".