Transforming Healthcare: Evaluating a Decade of Postgraduate Training at the Liberia College of Physicians and Surgeons
Bibliographic record
Abstract
BACKGROUND: Postgraduate training in Liberia was commenced in 2013 under the Liberia College of Physicians and Surgeons (LCPS). Since its inception, 125 medical doctors have specialized in seven disciplines: internal medicine, pediatrics, surgery, obstetrics and gynecology, family medicine, ophthalmology and psychiatry. This study evaluates the outcomes of a decade of postgraduate training in Liberia. METHODS: An online questionnaire was distributed to all graduates, collecting data on demographics, work history, motivation, self-assessed competencies, and training experiences, including examinations, research and feedback. Descriptive analysis was used to calculate response frequency distributions for quantitative outcomes, and thematic analysis was used for open answer responses. RESULTS: Ninety graduates (72.0%) responded to the questionnaire. All respondents worked in Liberia, with 56.3% in Montserrado County. Most (94.4%) were primarily employed in the public sector, whereas 21.1% held additional roles in the private sector or academia. After 3 years of membership-level postgraduate training to become a specialist, 30.0% pursued additional education, including 18.9% fellowship training. Overall, graduates expressed strong confidence in competencies gained during the postgraduate training, particularly medical knowledge, professionalism and teaching, with over 95% of graduates considering themselves competent in these areas. In contrast, only 44.3% felt competent in research. Nearly all graduates reported that the training supported their career growth and would recommend it to colleagues. Suggestions for improvements included increasing faculty numbers and diversity, enhancing training resources, and expanding research opportunities. CONCLUSIONS: The LCPS has contributed substantially to developing a competent specialist health workforce in Liberia. Graduates had a high retention rate and were primarily employed in clinical roles in the public sector. Challenges remain, such as too few specialists and limited healthcare coverage in rural areas. To train well-equipped specialists, it is crucial to strengthen faculty, institutional capacity and training resources, and to build research capacity. Sustained investment and continued collaborations between the government and partners are needed to implement strategies to maintain retention and improve equitable distribution of specialists and to support their career growth. These efforts will ensure the program's impact to meet Liberia's evolving healthcare demands effectively.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".