Addressing inequities in research for early to mid-career women scientists in low- and middle-income countries: “Supporting Women in Science” programme
Bibliographic record
Abstract
Introduction: The gender disparities and inequities faced by women in academia are widespread, especially in low- and middle-income countries (LMICs). The scholarly output of women scientists remains significantly lower than that of men due to limited opportunities. This widening gap has significant implications for policy-making and prioritizing agendas. The Supporting Women in Science (SWIS) programme aims to address these barriers by enhancing research capacity among early- and mid-career women scientists from LMIC regions such as South-Central Asia and East Africa, in bespoke areas of health and health-related sustainable development goals (HHSDGs). Methods: The SWIS programme utilizes online and distance learning with a self-paced approach. Applications are accepted on a rolling basis, through a pre-defined eligibility criterion. Phase I involves online self-learning courses covering a core and elective curriculum over 6 months which is then evaluated in Phase II. Eligible candidates then move to Phase III, a mentored fellowship where they develop research proposals and receive funding for research project development, implementation, and evaluation. The rigorous reporting and monitoring mechanisms track compliance and progress. The online format, offered at no cost, enhances program accessibility, particularly in the post-COVID era. Additionally, SWIS prioritizes mentorship by selecting experienced professionals with strong research backgrounds and mentorship skills to guide participants. The programme evaluation will be based on selected success metrics including program completion ratio, funding opportunities availed by the participants, and generated scholarly output and presentations at key events. Discussion and conclusion: Securing grant funding is pivotal for career advancement, yet women applicants face greater challenges as compared to men. The SWIS programme not only equips participants with knowledge and skills but also facilitates practical application through a simulated process, enabling participants to pursue future funding opportunities. Capacity-building initiatives like SWIS are crucial interventions to empower women scientists, foster equitable representation in academia, and create inclusive research environments and the programme acts as a steppingstone for future global leaders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".