Comparison of pharmacist knowledge, perceptions and training opportunities regarding maternal-fetal medicine in Canada, Qatar and Uganda
Bibliographic record
Abstract
BACKGROUND: Although pharmacists have great potential to modify and optimize drug therapy in pregnancy and lactation, current literature demonstrates that they do not routinely provide this care and often feel ill equipped to do so. The objective of this study was to determine pharmacists' knowledge and perceptions of maternal-fetal medicine in Canada, Uganda and Qatar. Secondary objectives were to determine factors associated with pharmacists' knowledge and to characterize training opportunities and resources available to practising pharmacists. METHODS: A cross-sectional survey using online software (SurveyMonkey) was sent to the e-mails of potential research participants. Practising pharmacists and resident pharmacists in British Columbia, Canada; the country of Qatar; and the country of Uganda were eligible for inclusion. The survey was designed to assess knowledge and perceptions, and to create a baseline inventory of current practice and information resources used in practice. RESULTS: The mean knowledge assessment scores of pharmacists in Canada, Qatar and Uganda were 62.9%, 53.3%, and 57.7%, respectively (p < 0.05). Pharmacists in British Columbia scored higher on knowledge assessment than pharmacists in Qatar (p < 0.05), but other country comparisons were not significant. No predefined factors (gender, years of experience, practice area or parental status) were found to be significant in determining the knowledge score. More than two-thirds of pharmacists expressed interest in participating in continuing education opportunities in maternal-fetal medicine. CONCLUSION: Pharmacists have differing levels of knowledge in the area of maternal-fetal medicine. Continuing education and degree curricula should be reviewed and developed to fill the knowledge gaps of student pharmacists and practising pharmacists in maternal-fetal medicine.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".