Cosmetic Surgery Regulation and Regulation Enforcement in Ontario
Bibliographic record
Abstract
In September 2007, 32-year-old Krista Stryland died from complications suffered after undergoing a liposuction procedure performed by a general practitioner without any surgical designation. This death brought the dearth of regulation in Ontario's private cosmetic surgery market to the public's attention. In response to the death, the College of Physicians and Surgeons of Ontario (CPSO) implemented reforms to provide oversight of private cosmetic surgery clinics. However, the nature and scope of the self-regulatory apparatus established in the wake of this fatality remains distinct from, and in the authors' opinion inferior to, the governmental oversight typical of facilities that dispense medically-necessary health services through the public healthcare system.It remains to be seen how rigorous the CPSO's self-regulatory regime will be. While numerous initiatives have been undertaken between 2008 and 2010, the authors discuss several institutional and professional disparities between the new self-regulatory regime for private cosmetic surgery and the existing regime for publicly funded cosmetic surgery. Inspections of private clinics through the Out-of-Hospital Premises Inspection Program, for example, are only required once every five years. If minimally adhered to, this regime will not approximate the licensing requirements that would have resulted from the direct government oversight of private clinics. The authors also highlight that from a professional perspective, self-regulatory regimes in jurisdictions such as British Columbia and Alberta have required surgical specializations, either explicitly or implicitly, before licensing health professionals to perform invasive surgeries. The CPSO has been unwilling to follow suit, and consequently its standards of care remain below those of its institutional contemporaries and provincial equivalents. The authors propose further regulatory options such as promoting informed patient choice, creating a licensing requirement for private sector clinics, and requiring accreditation for physicians performing invasive surgeries
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".