The Population-Centered Medical Model: a method of practice for public health physicians
Bibliographic record
Abstract
Abstract Background Public Health Physicians (PHPs) in Canada occupy a liminal space between medicine and public health. Current practice frameworks for the care of individual patients by physicians do not account for the complexity of working with communities and populations for health. Similarly, frameworks for public health practice do not account for the roles and responsibilities of physicians in public health. A method of practice that outlines how public health physicians care for populations is vital for training and development of practice for this specialty group within medicine and public health. Methods Constructivist Grounded Theory methods were used in this study. Ethics approval was obtained through The Western University Health Sciences Research Ethics Board. Semi-structured interviews were conducted with eighteen (18) currently practicing PHPs across Canada. Data was analysed iteratively using constant comparison, multi-level coding, and memo writing. Thick description and reflexivity were employed to enhance rigour. Results The key finding is the elucidation of the Population-Centered Medical Model. In this empirically grounded model, PHPs bring values, knowledge and stances to their practice of Public Health Medicine. PHPs consider the population as patient, along with ethical obligations that flow from the physician to the population as a consequence. The process of caring for populations involves both diagnosis and intervention, with a focus on systems and prevention. It relies on knowledge sharing and relationship building between the physician and the population. Conclusions This is the first empirical model to describe the practice of PHPs. The model firmly grounds PHP practice in both medicine (diagnosis and intervention, the construction of a patient) and public health (focus on populations, systems and prevention). It also presents an opportunity to develop a general model of medical practice for n patients, where n can range between 1 and N. Key messages • The Population-Centered Medical Model is an empirically grounded model that describes the work of Public Health Physicians in Canada and grounds practice firmly in medicine and in public health. • Public Health Physicians consider populations as patients. They diagnose and intervene for health, with a focus on systems and prevention, by building relationships and sharing knowledge.
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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 | 0.095 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".