Structured Multi-Stakeholder Workshops to Advance a Global Transformative Roadmap for Pharmaceutical Workforce
Bibliographic record
Abstract
In November 2016, the International Pharmaceutical Federation (FIP) endeavored to create an environment to foster a shared vision to lead a transformative pharmaceutical workforce roadmap. Three milestone documents were developed and presented at the Global Conference on Pharmacy and Pharmaceutical Education. Workshops with the key themes and connecting Pharmaceutical Workforce Development Goals (PWDG) were conducted and analyzed. This Note serves to summarize the key aspects of these workshops, reporting on the innovative approaches used to generate guidance for stakeholders regarding implementation. INNOVATION: Seven workshops with a uniform structure were developed. These were designed to improve communication, harmonise outcome-generation, and allow for aggregate analysis. A team of seven conducted each workshop, each team consisted of: a Chair, a facilitator, one rapporteur, and four speakers purposively selected from FIP member organisations and other key stakeholders with expertise for sharing a variety of perspectives. Guidelines and templates were developed for all roles and each team was briefed in advance. KEY FINDINGS: Approximately 200 personnel participated in the seven workshops, with around 20 country representatives per workshop, covering all six World Health Organisation regions. Three key aspects of workforce transformation, using the PWDGs, were explored in each workshop: drivers for implementation; challenges to implementation; and ways of encouraging implementation. Drivers for implementation mentioned were enhancing collaboration and engagement. Challenges to implementation were identified as variance in terminology. Several ways of encouraging implementation were acknowledged, such as communication strategies, advocating for workforce development and sharing best practices to foster partnerships. NEXT STEPS: The unique format of the workshops, the innovative approach to include stakeholders across an array of settings and the parallel structure in all the seven workshops, aided in creating reliable findings. The achievability of the PWDGs depends on several factors. Engagement with stakeholders and engagement from and between professional associations are important factors to achieving workforce development goals.
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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 | 0.078 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".