Health system drivers of hospital medicine in Canada: systematic review.
Bibliographic record
Abstract
OBJECTIVE: To identify the underlying systemic drivers of the development and ongoing expansion of hospitalist programs in Canada. DATA SOURCES: MEDLINE and Google Scholar were searched using combinations of the terms hospitalist, hospital medicine, and Canada. STUDY SELECTION: All publications that addressed the study question, including review articles, original research, editorials, commentaries, and letters or news articles, were included in the review. SYNTHESIS: Constant comparative methodology was used to analyze and code the articles and to synthesize the identified codes into broader themes. Three broad categories were identified: physician-related drivers, health system-related drivers, and patient-related drivers. Within each category, we identified a number of drivers. CONCLUSION: Many drivers have been cited in the literature as reasons behind the emergence and growth of the hospitalist model in the Canadian health care system. While their interplay makes simple cause-and-effect conclusions difficult, these drivers demonstrate that hospitalist programs in Canada have developed in response to a complex set of provider, system, and patient factors.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".