AI evidence extraction

Using AI to Increase Research Capacity: The Writing to Improve Nursing Science (WINS) in the Caribbean Program

Carolyn Sun, Roy A. Thompson, Oscar Ocho, Shelly-Ann Hunte, Shannon Harris · Studies in health technology and informatics · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.3233/shti260664

Methodology & findings

Study design

Pilot implementation study with a single cohort of participants tracking manuscript development and milestone achievement

Sample

N = 11, 2 groups

Main result

The pilot program demonstrated strong early outcomes, with "9 manuscripts are in active development, achieving 100% of early-stage milestones" among the initial cohort of 11 participants from Caribbean institutions, indicating the feasibility of AI-driven approaches to strengthen nursing research capacity in resource-limited settings.

Reports effect sizes.

Research paradigm

pragmatist/mixed-methods

Author conclusions

The pilot demonstrates that "AI-driven writing assistance" can feasibly "strengthen regional nursing research and scholarship" in resource-limited Caribbean settings, suggesting generative AI tools "hold promise for addressing global health inequities".

Risk of bias

selection_bias: small self-selected cohort (n=11); lack_of_control_group: no comparison condition described; attrition_risk: only 9 of 11 manuscripts in active development; reporting_bias: early-stage results without long-term outcome data; small sample size (n=11); no control group for comparison; single cohort without baseline comparison; potential selection bias in participant recruitment; Selection bias - small volunteer sample (n=11); Attrition potential - early-stage program with no reported follow-up beyond initial milestones; Potential social desirability bias in self-reported progress

Open questions raised

The paper indicates that "the potential to support scholarly productivity in resource limited settings, remains underexplored," suggesting a need for further investigation of AI tools in global health equity contexts.
The paper indicates that "the potential to support scholarly productivity in resource limited settings, remains underexplored," suggesting this as a key gap that the WINS program aimed to address.
The paper indicates that the "potential to support scholarly productivity in resource limited settings, remains underexplored," suggesting a gap in evidence for AI-supported capacity building in under-resourced regions.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 71%

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