Bibliographic record
Abstract
Background: Family physicians deliver medical care for long-term care residents in Canada. These physicians provide care with varying levels of commitment, but there are knowledge gaps concerning the relationship between physician practice and care quality. No consensus exists on how to define and measure physician commitment in long-term care. Objectives: This thesis examined: (1) How does physician commitment to long- term care influence resident care quality? (2) What are the relevant and feasible expectations for physician commitment in long-term care? (3) How does the long-term care practice environment influence medication prescribing patterns? Methods: This thesis comprised a retrospective cross-sectional study, a modified e-Delphi study, and a retrospective cohort study. Using health administrative data, the commitment of long-term care physicians was described and examined in relation to resident outcomes. Expert physicians rated statements to define expectations of commitment. A multi-level cohort study analyzed electronic medical records of long-term care residents to examine prescribing variation at the resident, unit, home, and organizational levels. Results: Only 114 (8.3%) physicians had a practice commitment of 80% or higher, which was associated with 10% fewer emergency department visits, but showed inconsistent relationships with other outcomes in administrative data. The consensus study established that commitment is multidimensional, encompassing 21 statements that define commitment. Lastly, this work examined the long-term care practice environment in a multi-level analysis and found substantial prescribing variation across psychotropic and non-psychotropic medications, with home variation consistently exceeding unit variation. Conclusion: This thesis provides the first comprehensive understanding of physician practice in Canadian long-term care homes. The studies developed an approach to measure commitment within health administrative data and demonstrated that commitment is multidimensional, with care quality resulting from complex interactions between physician characteristics and organizational contexts. This thesis provides support for evidence-based guidance for medical education, workforce planning, and quality improvement initiatives.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".