Bibliographic record
Abstract
Background: In 1986, Cosmair Canada, agent of the L'Oréal Group, sponsored a survey among Canadian dermatologists to measure the perceptions, attitudes, expectations, and needs of dermatologists regarding cosmetology. Fifteen years later, a new survey among Canadian dermatologists, again sponsored by L'Oréal, tries to capture the evolution and new trends. Objectives: This survey tries to capture the perceptions, attitudes, and expectations of Canadian dermatologists regarding cosmetology. Methods: A questionnaire was sent to 394 members of the Canadian Dermatology Association; 99 responded (25%). The questionnaire, had 21 questions regarding cosmetology, future trends in Canadian dermatology, and how dermatologists acquire the information required for their practice. Results: More than ever, dermatologists are asked to deal with maintaining healthy, youthful skin. Cosmetology is a greater part of everyday practice. Laser and cosmetic dermatology will become more important in the future. Conclusions: Dermatologists are asked to provide information on the maintenance of healthy, young-looking skin and feel they need more information relating to cosmetology.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 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".