Medical education and population health—A framework in the design of a new undergraduate program
Bibliographic record
Abstract
Health sciences curricular planners are challenged to add new content to established education programs. There is increasing pressure for content in public health, health systems, global health, and planetary health. These important areas often compete for curricular time. What is needed is a convergence model that builds a common framework within which students can integrate areas and better align this knowledge to the individual client or patient who they have responsibility to support. A population health framework is proposed for health sciences education programs that supports a common conceptual understanding of population health. The framework links five thematic areas that have influence on health and wellbeing and a sixth element that defines the range of methodologies essential to understanding health and wellbeing, from the individual to the population. The five areas providing convergence are: (1) the biopsychosocial development of the individual, (2) the socioeconomic factors that influence health and wellbeing, (3) the physical natural and built environment including climate, (4) the continuum of public health and health care systems, and (5) the nation state and global relationships. Using this framework, students are encouraged to think and understand individual health and wellbeing in context to the population and to utilize the appropriate methodological tools to explore these relationships. Planning for a new undergraduate medicine program illustrates the curricular elements that will be used to support student learning with foundation knowledge applied and tracked throughout the program. The proposed framework has application across health sciences disciplines and serves to build a common understanding that supports cross professional communication and collaboration.
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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 | 0.038 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".