Capacity-building in family health
Bibliographic record
Abstract
PROBLEM BEING ADDRESSED Brazil, Chile, and Canada are among the countries where development and deployment of human resources have been central to health reform; however, it is unclear how the education and training of primary care workers is best accomplished. OBJECTIVE OF THE PROGRAM To implement a model of in-service training in primary health care for interdisciplinary teams of primary health care professionals from Brazil and Chile. PROGRAM DESCRIPTION This 5-module program targeted primary care providers from various disciplines who had at least 3 months of front-line experience. The program was offered in 2 formats: intermittent “in-country” training or an intensive course taught in Canada. In Brazil, the in-country training took place over a period of 8 to 12 months, during which 5 modules of 2 to 3 days each were interspersed with 2-month “action periods.” The intensive course taught in Canada was delivered to Chilean participants in Toronto, Ont, where 3 modules were offered to a group of 12 to 20 primary health care professionals over a 6-week period. The educational methodology combined short didactic presentations, whole group learning exercises, and small group problem-based learning sessions, including team projects that were completed in between each module and presented at the beginning of the next one. During the course, the participants learned how to perform computer database searches and assess the best evidence in the management of common problems. CONCLUSION Pretests, posttests, and evaluations of student projects demonstrated that participants had increased knowledge, as well as increased capacity to use the best evidence to address common problems in their communities. This is a promising model, adapted to the context of primary care reform in Latin America, with strong potential to support health human resource development and multidisciplinary care by front-line providers in other countries.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 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".