Computer use in primary care practices in Canada.
Bibliographic record
Abstract
OBJECTIVE: To examine the use of computers in primary care practices. DESIGN: The international Quality and Cost of Primary Care study was conducted in Canada in 2013 and 2014 using a descriptive cross-sectional survey method to collect data from practices across Canada. Participating practices filled out several surveys, one of them being the Family Physician Survey, from which this study collected its data. SETTING: All 10 Canadian provinces. PARTICIPANTS: A total of 788 family physicians. MAIN OUTCOME MEASURES: tests comparing new and traditional models of primary care on measures of computer use and electronic health record (EHR) use, as well as descriptive statistics. RESULTS: < .001; odds ratio = 2.15). Overall, there was a statistically significant variability in computer use across provinces. CONCLUSION: Most family physicians in Canada have incorporated computers into their practices for administrative and scholarly activities; however, EHRs have not been adopted consistently across the country. Physicians with practices operating under the new, more collaborative models of primary care use computers more comprehensively and are more likely to use EHRs than those in practices operating under traditional models of primary care.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".