Evolution of Family Medicine in Kenya (1990s to date): a case study
Bibliographic record
Abstract
Background: Successful Family Medicine practices and academic programmes are found in western countries, Australia, Singapore, Cuba and among other non-western countries. Documenting the enablers and challenges of different contexts would, it is hoped, inform current and future process of developing academic and practice programmes in Family Medicine in countries where the discipline is starting. Methods: A qualitative study was undertaken that conducted a focused literature review and in-depth interviews of key informants on the early development of the Family Medicine in Kenya. All interviews were audio recorded. Pattern matching, explanation building, time-series analysis and logic models were used in analysis. Results: Representatives of Kenyan and foreign organisations worked well as a team to write and implement the first curriculum of Family Medicine. The challenges include lack of teachers; starting a graduate programme in medical schools that did not have one and starting these health services delivery departments in a system that did not have any. Conclusions: The main enablers of the evolution of Family Medicine in Kenya include committed partnerships and teamwork among Kenyan and non-Kenyan stakeholders. The challenges include the lack of Kenyan teachers of the programme and the introduction of a new discipline. (Full text of the research articles are available online at www.medpharm.tandfonline.com/ojfp) S Afr Fam Pract 2017; DOI: /10.1080/20786190.2016.12481420
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".