The Commonwealth Fund Survey of Primary Care Physicians Reveals Challenges Experienced by Family Doctors and Emphasizes the Need for Interoperability of Health Information Technologies
Bibliographic record
Abstract
Electronic health information that is easily accessible and shareable among healthcare providers and their patients can provide substantial improvements in Canada's primary care system and population health outcomes. The Commonwealth Fund's (CMWF's) 2022 International Health Policy Survey of Primary Care Physicians (CIHI 2023) highlights the views and experiences of primary care doctors in 10 developed countries, including Canada. The survey covered various topics related to physician workload, the use of information technology and coordination of care. While the COVID-19 pandemic contributed to an increased physician workload that may have impacted the ability to efficiently coordinate care with other healthcare providers, Canadian family doctors did close the gap with other countries as 93% of family doctors are now using electronic medical records (EMRs) in their practices. The CMWF's 2022 survey revealed challenges faced by Canadian family doctors in their practices. However, international comparisons provide opportunities to learn from other countries and build on the implementation of EMRs as part of Canada's shared health priorities.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".