Women in pharmacy
Bibliographic record
Abstract
BACKGROUND: Women have historically been attracted to pharmacy because it is widely perceived as a profession that offers them an opportunity to combine a professional career with a family. Women now make up the majority of practising pharmacists in Canada, yet the literature demonstrates disparities such as gender segregation and underrepresentation of women in senior positions. This study was intended to identify the attitudes and beliefs of pharmacy students about women's issues in pharmacy and raise awareness of these issues. METHODS: First- and fourth-year University of Saskatchewan pharmacy students were invited to share their overall impressions of the status of female pharmacists and the impact of women on the pharmacy profession through an online questionnaire. RESULTS: Of the 60 respondents, the majority disagreed that there is segregation of men and women in pharmacy. More fourth-year students than first-year students recognized the underrepresentation of women in pharmacy management. Many students believed the number of women in pharmacy would have no negative impact on the profession. Forty students (67.8%) agreed that it is important to maintain a significant proportion of men in pharmacy. CONCLUSION: Most pharmacy students in this study do not recognize gender disparities present in pharmacy or the impact the disproportionate number of women could have on the profession. Can Pharm J 2013;146:109-116.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.002 |
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".