Features of physician services databases in Canada
Bibliographic record
Abstract
INTRODUCTION: Physician services databases (PSDs) are a valuable resource for research and surveillance in Canada. However, because the provinces and territories collect and maintain separate databases, data elements are not standardized. This study compared major features of PSDs. METHODS: The primary source was a survey of key informants that collected information about years of data, patient/provider characteristics, database inclusions/exclusions, coding of diagnoses, procedures and service locations. Data from the Canadian Institute for Health Information's (CIHI) National Physician Database were used to examine physician remuneration methods, which may affect PSD completeness. Survey data were obtained for nine provinces and two territories. RESULTS: Most databases contained post-1990 records. Diagnoses were frequently recorded using ICD-9 codes. Other coding systems differed across jurisdictions and time, although all PSDs identified in-hospital services and distinguished family medicine from other specialties. Capture of non-fee-for-service records varied and CIHI data revealed an increasing proportion of non-fee-for-service physicians over time. CONCLUSION: Further research is needed to investigate the potential effects of PSD differences on comparability of findings from pan-Canadian studies.
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.047 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".