كوفيد-19 خلق الفرص من الأزمات لطلاب الصيدلة: نقاش من حول العالم
Bibliographic record
Abstract
Qatar University’s College of Pharmacy (QU-CPH) and Qatar Pharmacy Undergraduate Society (QPhUS) conducted a webinar entitled “ COVID-19 Creating Opportunities from a Crisis for Pharmacy Students: Discussion from Around the Globe”. The event was held successfully via WebEx platform with more than 100 attendees from around the world and more than 300 views on YouTube. Speakers at the event included student leaders and members of pharmacy associations that are part of the International Pharmaceutical Students' Federation. They were: Hend Al-Naimi, QPhUS President and CPH fourth professional year student;. Ghulam Mujtaba, Member of Pakistan Pharmaceutical Students’ Federation (PPHSF);. Wafa Othman Member of Palestinian Pharmaceutical students’ federation An-Najah National University (PSFNNU); Melissa Kieley, Canadian Association of Pharmacy student and Interns Contact Person (CAPSI); and Ismail Jomha, Vice president chairperson of the Professional Development Committee at the Lebanese Pharmacy Students’ Association (LPSA). The webinar aimed to share the different experiences by pharmacy students around the world and how pharmacy students around the globe created new stories of success despite the unprecedented circumstances the world is facing.
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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 | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.174 | 0.106 |
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".