Generalizability Across Populations and Settings
Research gap analysis derived from 2 computer_science papers in our local library.
The gap
There is a need for larger, more diverse sample sizes and multi-center studies to validate findings across different populations and settings.
Consensus across the literature
The papers collectively establish the importance of generalizability but leave open how to achieve it through specific methodologies.
Research trend
Emerging — attention growing, methods still coalescing.
Supporting evidence — 2 representative gaps
- The role of anesthesia in TKA length of stay: a data-driven analysis using Boruta algorithm (2026) · doi
The single-center setting limits the external validity across diverse healthcare systems and populations.
Keywords: single center setting limits external validity across diverse healthcare systems populations - The mediating and moderating role of mathematical problem-solving skills in the relationship between primary school students number sense and mathematics self-efficacy (2026) · doi
Future studies employing stratified or multistage sampling designs with larger regional representation may strengthen external validity.
Keywords: future employing stratified multistage sampling designs larger regional representation strengthen external validity
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