Bibliographic record
Abstract
Rachel Podell and her colleagues at the National Program for Quality Indicators in Community Healthcare in Israel have provided a clear and engaging description of thequality of primary care provided to the elderly in Israel. They examine changes overtime, variation across sub-groups, and comparisons with other countries. Over a 13year timeframe, most of the included process measures improved substantially, withfairly minor differences between demographic groups and largely favourablecomparisons with other countries. In the Podell et al article, there are few direct comparisons of primary care available between Canada and Israel, but we know from other studies that Canadian primary care compares relatively unfavourably with ten other developed countries in a number of measures. These include timely access to care, after-hours care, electronic medical record use and audit and feedback for quality improvement. More concerning is that few of these measures have improved in Canada over a number of years, despite a major policy focus on primary care, investments in payment reforms and the formation of groups and inter-professional teams. Differences in performance trajectories could relate to the major structural differences in primary care between Canada and Israel. While Canada has universal health insurance coverage for necessary physician and hospital services, most physicians practice privately, are paid mainly through fee-for-service and have few accountabilities to the health care system. Many Canadians lack a regular source of primary care and there is little or no competition between primary care practices or groups, as most of them have full practices, and are not accepting new patients. No Canadian province has completely implemented electronic medical records in primary care. Canada also lacks the organizational, administrative and support structures of Israel's health maintenance organizations. Canada and other countries can learn from the advances in data, measurement, feedback, and organization of care that are now applied routinely to ongoing quality improvement in Israel, with impressive results. The Israeli experience suggests that future developments designed to improve care and outcomes should include measurement infrastructure, formal reporting, accountability mechanisms, and management systems to address gaps and inequities in care.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".