Bibliographic record
Abstract
Background: As pharmacy practice evolves, an increasing number of pharmacists are diversifying into non-traditional roles such as aesthetic procedures. Despite this shift, comprehensive research exploring demographic patterns, motivations, practice settings, and perceived support and barriers among pharmacists in this specialty, particularly in Alberta, Canada, remains scant. Objectives: This study aimed to explore the demographics, experience, practice settings, types of aesthetic services offered, and perceptions of support from the Alberta College of Pharmacy (ACP) among Alberta pharmacists. It also sought to understand the relationship between pharmacists' years of practice and the complexity of the aesthetic services they provide and to assess the impact of contacting the ACP on their perceptions of clarity in the aesthetic injection regulatory framework. Methods: An online survey was disseminated to registered pharmacists in Alberta, gathering data on demographics, experience, practice settings, aesthetic services offered, and perceived barriers and support. Statistical analysis was used to identify significant trends and associations, employing Chi-square, Kendall's tau-c, and Gamma tests. Results: The results showed a significant association between younger age and involvement in aesthetic practice, with a substantial presence in hospital settings. Part-time pharmacists showed stronger involvement, and those with less than five years of experience were more likely to practice aesthetic injections. No statistically significant relationship was found between the years of practice and the complexity of aesthetic services. Clear dissatisfaction was evident regarding the support and communication from the ACP, with contact with the ACP not necessarily resulting in clarity regarding aesthetic practice regulations. Conclusion: The findings suggest that aesthetic practices are becoming a significant part of pharmacy practice, particularly among younger, part-time pharmacists associated with hospital settings. However, this shift calls for improved support, clearer guidelines, and better communication from the ACP, particularly around aesthetic practice regulations.It also emphasizes the need for regulatory bodies to ensure pharmacists are equipped to offer these services safely and effectively, which could be crucial in shaping future policy-making and professional development initiatives.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".