Adapting the American society of hematology crti-la model to build clinical research capacity in Mexico: Results from the ameh-I3 course
Bibliographic record
Abstract
Abstract Introduction: Building research capacity is essential for advancing evidence-based clinical practice. However, access to structured research training remains uneven, particularly in low and middle-income countries, posing a major barrier to scientific development. Given these limitations, adapting existing high-quality international programs to local contexts represents a promising strategy. Objective: To adapt the American Society of Hematology's Clinical Research Training Institute in Latin America (CRTI-LA) model, a successful international in-person training experience that educates international early career hematologists and fellows, to the Mexican context, and explore its impact on self-reported research capacity development. Methods: We conducted a pre-post study to assess the changes in perceptions, and research confidence among early-career hematologists who participated in the adapted CRTI-LA course. Eligible participants were hematology fellows, or early-career clinical hematologists actively working in Mexico. Fourteen individuals were selected through a competitive national call issued by the Agrupación Mexicana para el Estudio de la Hematología (AMEH). Selection criteria included trainees with a clinical focus, ≤15 years post-training, a research question, and motivations for developing investigator-initiated research. The “AMEH I3 course” took place on Veracruz on October 2024. The 2-day spanish language program was designed to cover a range of essential topics in clinical research including how to formulate research questions and study designs, effective data management, statistical methods, ethical considerations and the use of patient-reported outcomes in clinical trials. Additional modules focused on scientific writing and the publication process, aiming to equip participants with the skills necessary to prepare abstracts and manuscripts suitable for peer-reviewed journals.Teaching methods were diverse and interactive, consisting of didactic lectures, small-group discussions, case-based learning exercises, and personalized one-on-one mentorship sessions. Faculty members included past CRTI-LA trainees and faculty, senior researchers and clinicians with expertise in clinical research and hematology, representing both national and international academic institutions. The primary endpoint was to evaluate the impact of the course on participants’ self-reported research skills and attitudes 6 months after the course using an electronic form using a verbal Likert format (strongly aggree to strongly disagree) and additional open-ended questions to capture qualitative insights. Results: Thirteen of 14 participants answered the follow-up survey (92%). Over 80% rated all components of the course: presentations, small group sessions, networking meetings, and one-on-one mentorship sessions as “useful” or “very useful”. Most (77%) found the content of the course relevant for their current position, 69% agreed and 23% strongly agreed to feel confident performing research in their field, 92% strongly agreed and 8% agreed that the knowledge and skills of the I3 course would be fundamental for their capacity to perform research. Confidence in key research domains were high: 84% felt confident in preparing a solid clinical research proposal; 92% in developing a solid scientific hypothesis; 92% with the basics of developing a grant proposal. Areas of less confidence included skills for observational epidemiological research, opportunities to collaborate with peers and clinical research leaders in the field. The most useful components identified were stasitics (n=7), one-on-one mentorship (n=3), and study design (n=3). Thematic analysis of open-ended responses revealed overwhelmingly positive perceptions, citing the program as “an excelent opportunity for professional and personal growth”. Suggested improvements included dedicating more time to specific topics and deeper emphasis on funding sources. Participants also expressed a strong desire to see the course countinued: “Hopefully it will continue for future generations.” Conclusions: The AMEH-I3 course, a Mexican adaptation of the ASH CRTI-LA model, was successfully implemented and well-received by participants. These results suggest that this model can be effectively replicated by CRTI-LA alumni in LMICs through sustained national and international collaboration. The second iteration of AMEH-I3 will take place in October 2025.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".