Family Medicine for internally displaced persons in Mali: A training of trainers approach
Bibliographic record
Abstract
Mali is currently experiencing a polycrisis, characterised by the interplay of growing socio-political insecurity, massive population displacements and recurrent tensions in the functioning of the health system and the provision of care. In this context, the multidisciplinary teams of University Community Health Centres (CSCoM-Us), primary health care facilities, have expressed the desire to strengthen their skills to better meet the needs of the internally displaced persons who frequent their facilities. To address this demand, training workshops were co-constructed by a team of family physicians (FPs), combining the experiential knowledge of local teams with the expertise of partners from the Université de Sherbrooke. A Training of Trainer (ToT) programme, consisting of training provided by central-level trainers to local-level practitioners, was developed and deployed. Five priorities were identified by local partners: continuity of care, maternal health, gender-based violence, mental health and working with a language barrier. From 2022 to 2023, this training was implemented in Mali's seven CSCOM-Us, reaching 277 health professionals in five regions of the country. The outcomes include increased awareness of the challenges faced by internally displaced persons and strengthening local capabilities. This short report highlights the strategic role and leadership played by FP in improving the population's health, particularly in sub-Saharan Africa, through their versatility and community-oriented, holistic and patient-centred approach.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".