Introducing the Objective Structured Clinical Examination in Haiti
Bibliographic record
Abstract
Haiti, a nation of approximately 10.7 million people located in the western Caribbean,1 is a low-income country, with 59% of its population living below the poverty line (less than $2.41 per day), and 24% living in conditions of extreme poverty (less than $1.23 per day).2 Haiti has a history of political instability and natural disasters, and remains the country with the highest rate of poverty in the Americas.2The health care system in Haiti is regulated by the Ministry of Public Health and Population (MSPP). MSPP is under-resourced, spending only US $13 per person on health care each year. This represents a mere 6.1% of the national budget, and is significantly less than its neighboring countries of Cuba (US $781) and the Dominican Republic (US $180).3Six universities in Haiti offer degree programs in medicine. Upon graduation, medical school graduates are required to complete 1 year of social service, and then can practice medicine independently or apply for entrance into one of Haiti's 37 residency programs.Zanmi Lasante (ZL), a sister organization to Boston, Massachusetts-based Partners In Health, in partnership with MSPP, offers 1 residency program at Hospital St. Nicolas (HSN) in Saint-Marc and 5 programs at Hôpital Universitaire de Mirebalais (HUM) in Mirebalais. The programs that have been accepted are in the preapproval phase with the Accreditation Council for Graduate Medical Education International (ACGME-I).As part of its system of assessment in 2015, we introduced the objective structured clinical examination (OSCE) in the family medicine residency program at HSN, with the help of 2 Canadian fellows who volunteered in Haiti. Faculty members were trained on the new technique and were given opportunities to practice their new skills. These “pioneers” then trained other faculty at HUM. Although widely used in high-income countries, ZL was the first institution in Haiti to use the OSCE.The OSCE is a group of tests that includes a succession of stations with simulated clinical problems, involving standardized patients or mannequins, that learners need to solve in a limited time. Each station has clearly defined objectives, and a checklist for the evaluation of the candidates.4 It is considered the gold standard for evaluating clinical competencies, including the physician-patient relationship, the physical examination, and interpersonal and communication skills (box).4In 2016, the Director of Graduate Medical Education at ZL implemented an OSCE as a pre-assessment tool during the orientation month at the start of postgraduate year 1 (PGY-1). Based on this positive experience, the Graduate Medical Education Committee (GMEC) voted to implement the OSCE as part of the recruitment process for all ZL programs. In 2017, we conducted the first OSCE session during recruitment, with 90 candidates who aspired to enter 1 of the 6 ZL residency programs.Each session consisted of 4 themed stations with different objectives and a break station. The duration of each station was 10 minutes. The OSCE was scored by faculty, the training and research department director, and representatives from the school of medicine.The OSCE was a new evaluation tool for medical educators in Haiti, and implementation faced challenges. We needed to train faculty and the standardized patients (SPs). Because of a lack of funds, we used medical students and residents as SPs. The use of medical students as SPs was helpful in multiple ways, allowing the students to learn clinical portrayal and trainee performance. Candidates and student SPs were nervous at first, but by the end of the day, all said they had benefited from the experience.We are currently testing the OSCE as an evaluation tool in 2 residency programs (anesthesiology and pediatrics), and we intend to implement it in all ZL residency programs.
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 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".