Strengthening public health education and humanitarian response through academic volunteerism
Bibliographic record
Abstract
INTRODUCTION: Volunteers are an integral part of the International Red Cross and Red Crescent (RCRC) Movement, with over 16 million people actively contributing to humanitarian action worldwide. Academic volunteerism within the Movement includes contributions from students, volunteers and professionals from academic institutions who offer their time and expertise. In this study we aimed to understand the process of embedding academic volunteers in humanitarian organizations such as the Canadian Red Cross (CRC) and assess the impact of their activities within the realm of public health education. METHODS: We used a qualitative case study design with an instrumental approach. All documents related to academic volunteers within the CRC database from September 2018 to August 2023 were gathered and reviewed. Data related to the processes around engaging with volunteers, timelines, outcomes and feedback surveys from students and staff members were extracted and a content analysis was conducted. A return-on-investment analysis (ROI) was conducted to assess the financial impact of engaging academic volunteers. RESULTS: A total of 68 academic volunteers were engaged with CRC, including unpaid or partially paid master's students, doctoral and postdoctoral fellows, and student volunteers. The collaboration between CRC and academic volunteers contributed to educational enrichment, professional development, knowledge transfer, operational efficiency, and talent pool expansion. Results from a survey on academic volunteerism further highlighted benefits such as maintaining project schedules, promoting diversity, and amplifying the Movement's voice on important matters. The return on investment for unpaid academic volunteers and Masters students was 70%. A five-fold increase was measured for partially paid academic volunteers resulting in 486% ROI. DISCUSSION: Academic volunteerism yields mutual benefits for students, academic researchers, and humanitarian organizations. These volunteers enhance operational efficiency and fortify resilience, fostering adaptability amid challenges. They play a crucial role in strengthening evidence-based programming within organizations like CRC and bolstering their capacity to address emerging health issues. These volunteers constitute a valuable talent pool, bringing organizational knowledge and experience to the table. They provide critical support for scaling up programs, especially during emergency situations, offering innovative solutions to address human resource shortages driven by funding constraints.
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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 | 0.024 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".