Gastroenterology Practitioner and Trainee Numbers in Canada 2018: Annual Report From the Canadian Association of Gastroenterology
Bibliographic record
Abstract
BACKGROUND: It is necessary for health planners, educators and physician and patient organizations to be aware of trends in gastroenterologist (GI) numbers in order to ensure that patients have timely access to care. METHODS: The number of GIs in practice and the number of trainees in the specialty was determined for 2018 using three national databases compared with previous years. RESULTS: In 2018, there were 787 GIs in Canada, which equated to 2.1 GIs per 100,000 population. There are marked differences between provinces with numbers ranging from 1.1 to 2.9 per 100,000. There are 53 GIs specializing in pediatric GI care. Forty-six per cent of practitioners under the age of 35 years are female. Seventy-two residents are training in adult GI and six in pediatrics. Approximately 75% of fellows in adult and pediatric GI are training on temporary visas. The number of adult GIs is decreasing despite increasing national population growth and service demand. The numbers of trainees in both adult and pediatric GI are lower than in 2010. If these trends continue, wait times for GI care, which are already poor, will likely increase further. CONCLUSIONS: Continued monitoring of human resource numbers, patient access to care and validation of current data is required.The purpose of this report is to present the number of gastroenterologists (GIs), both in practice and in training, in Canada for 2018. We also wished to examine the 2018 numbers by province and gender, compare the 2018 numbers with those of previous years and describe the practice settings and organization.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".