17. Boundaries or overlap: An examination of the community-oriented clinical practice of community medicine specialists and family/general practitioners
Bibliographic record
Abstract
We compared the work settings and “community-oriented clinical practice” of Community Medicine (CM) specialists and family physicians/general practitioners (FP). We conducted secondary data analysis of the 2004 National Physician Survey (NPS) to examine main work setting and clinical activity reported by 154 CM (40% of eligible CM in Canada) and 11,041 FP (36% of eligible FP in Canada). Text data from the specialist questionnaire related to “most common conditions that you treat” were extracted from the Master database for CM specialists, and subjected to thematic analysis and coded. CM specialists were more likely than FP to engage in “community medicine/public health” (59.7% vs 15.3%); while the opposite was found for primary care (13% vs. 78.2%). CM specialists were less likely to indicate a main work setting of private office/clinic/community health centre/community hospital than were FP (13.6% vs. 75.6%). Forty-five percent of CM provided a response to “most common conditions treated” with the remainder either leaving the item blank or indicating that they did not treat individual patients. The most frequently named conditions in rank order were: psychiatric disorders; public health program/activity; respiratory problems; hypertension; and metabolic disorders (diabetes). There is some overlap in the professional activities and work settings of CM specialists and FP. The “most commonly treated conditions” suggest that some CM specialists may be practicing primary care as part of the Royal College career path of “community-oriented clinical practice.” However the “most commonly treated conditions” do not specifically indicate an orientation of that practice towards “an emphasis on health promotion and disease prevention” as also specified by the Royal College for that CM career path. This raises questions about the appropriateness of the current training requirements and career paths as delineated for CM specialists by the Royal College of Physicians & Surgeons of Canada. Bhopal R. Public health medicine and primary health care: convergent, divergent, or parallel paths? J Epidemiol Community Health 1995; 49:113-6. Pettersen BJ, Johnsen R. More physicians in public health: less public health work? Scan J Public Health 2005; 33:91-8. Stanwell-Smith R. Public health medicine in transition. J Royal Society of Medicine 2001; 94(7):319-21.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".