Access to Electroconvulsive Therapy Services in Canada
Bibliographic record
Abstract
OBJECTIVES: We sought to determine factors governing access to electroconvulsive therapy (ECT) in Canada. METHODS: We contacted all 1273 registered health care institutions in Canada and invited the 175 centers identified as providing ECT to complete a comprehensive questionnaire. To determine geographic access to ECT, we used a geographic information system, population density data, and road network data. Responses to 5 questions from the questionnaire were used to identify local barriers to access. RESULTS: Approximately 84% of the population in the 10 Canadian provinces live within a 1-hour drive of an ECT center, but 5% live more than 5 hours' drive away. There was significant province-to-province variation, with all of the citizens of Prince Edward Island living within 2 hours of an ECT center but 12.5% of those in Newfoundland and Labrador living more than 5 hours' distance away. There are no ECT services at all in the 3 territories, which contain 3% of the Canadian population. Nongeographic barriers to access included inadequate human resources, particularly, a lack of anesthesiologists, in 59% of the centers; logistical impedances (52%); space limitations (45%); strictures on the hiring of adequate staff (29%); imposed limits to number of treatments or to operating or postanesthetic room time (28%); and a lack of funds to purchase up-to-date ECT or related anesthesiology equipment (14%). CONCLUSIONS: Electroconvulsive therapy is geographically accessible for most Canadians. Even when geography is not a factor, however, there are significant barriers to access resulting from inadequate availability of qualified professional staff, treatment areas, and funding.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".