A Needs Assessment of the Number of Comprehensive Addiction Care Physicians Required in a Canadian Setting
Bibliographic record
Abstract
OBJECTIVE: Medical professionals adequately trained to prevent and treat substance use disorders are in short supply in most areas of the world. Whereas physician training in addiction medicine can improve patient and public health outcomes, the coverage estimates have not been established. We estimated the extent of the need for medical professionals skilled in addiction medicine in a Canadian setting. METHODS: We used Monte-Carlo simulations to generate medians and 95% credibility intervals for the burden of alcohol and drug use harms, including morbidity and mortality, in British Columbia, by geographic health region. We obtained prevalence estimates for the models from the Medical Services Plan billing, the Discharge Abstract Database data, and the government surveillance data. We calculated a provider availability index (PAI), a ratio of the size of the labor force per 1000 affected individuals, for each geographic health region, using the number of American Board of Addiction Medicine certified physicians in each area. RESULTS: Depending on the data source used for population estimates, the availability of specialized addiction care providers varied across geographic health regions. For drug-related harms, we found the highest PAI of 23.72 certified physicians per 1000 affected individuals, when using the Medical Services Plan and Discharge Abstract Database data. Drawing on the surveillance data, the drug-related PAI dropped to 0.46. The alcohol-related PAI ranged between 0.10 and 86.96 providers, depending on data source used for population estimates. CONCLUSIONS: Our conservative estimates highlight the need to invest in healthcare provider training and to develop innovative approaches for more rural health regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".