Three Papers on Physician Labour Supply in Canada
Bibliographic record
Abstract
Despite Canada's record-high physician-to-population ratio, persistent wait times for specialist healthcare and insufficient primary healthcare access raise questions. Why does Canada face medical service shortages notwithstanding its high physician count per capita? What factors should be accounted for in physician workforce planning? To address these questions, I analyze Statistics Canada's population estimates and Labour Force Survey (LFS), and the Canadian Institute for Health Information's (CIHI's) physician expenditure and socio-demographic data, from 1987 to 2021. I focus predominantly on the supply side but also consider the demand side. In the first paper, I show that despite a 35% increase in physicians per capita from 1987 to 2019, the growth rate adjusted for physician labour supply and population aging is negative four percentage points. A 20% reduction in physician work hours from 1987 to 2020 contributes to this decline. These findings underscore the importance of considering factors beyond physician counts. In the second paper, examining physicians' COVID-19 responses, I find a statistically significant reduction in work hours during the first wave, with a subsequent recovery to the pre-pandemic level. The net reduction was entirely in community settings, with no statistically significant difference between general practitioners/family physicians and specialists. Moreover, no statistically significant gender differences were observed. In the third paper, I investigate factors contributing to the declining physician work hours using the LFS– a general-purpose survey. As the LFS survey weights are not designed for physician-specific analysis, I apply a generalized method of moments (GMM) weighting technique using CIHI's physician population data that improves estimation quality. This illustrates how the bias and/or precision of general-purpose surveys can be improved in profession-specific analyses. Reduced hours among males, the increased share of females, workforce aging, and an increase in absence rates and lengths are key reasons behind the decline.
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.049 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".