Improving Patient Access to Medical Services: Preventing the Patient from Being Lost in Translation
Bibliographic record
Abstract
Case Study Introduction/BackgroundImproving access to health services is a priority across Canada.The data on Canada's performance with regard to access to primary and specialty care suggests a significant opportunity for improvement.For example, in 2004, Canada was identified as the country with the lowest percentage of citizens who could access a physician with a same-day appointment (27%), compared to the United States (33%), the United Kingdom (41%), Australia (54%) or New Zealand (60%) (College of Family Physicians of Canada 2006).With regard to access to specialty care, Canada ranked second lowest, with 57% of its citizens waiting at least four weeks to access specialty care, compared to the United States (60%), Australia (46%), the United Kingdom (40%), Germany (23%) and New Zealand (22%) (College of Family Physicians of Canada 2006).Nationally and internationally, there has been significant research on wait times.Postl reports, however, that "wait times are a symptom of a larger problem… Canadians need to support a transformation that puts patients at the centre of the system" (Postl 2006: 9).In the final report of the Federal Advisor on Wait Times, recommended actions to improve access included research to support benchmarking and operational improvements, adoption of modern management practices and innovation, accelerated implementation of information technology solutions and cultural change among health professions (Postl 2006).The challenge is navigating change across multiple healthcare service providers in diverse settings across the continuum of care.Change strategies that support access and integration include providing people-centred care, reducing clinical variance, organizing the care continuum and improving process management.These strategies became the major focus of the improvements implemented in Calgary.
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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 | 0.028 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".