השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| הערכה פרגמטית של מבחני סקר× | מחקר אפידמיולוגי חתך× | |
|---|---|---|
| תחום | אפידמיולוגיה | אפידמיולוגיה |
| משפחה | Process / pipeline | Process / pipeline |
| שנת המקור≠ | 2000s-2010s (formalized with PRECIS framework) | 1960s (formal codification); widely practiced since mid-20th century |
| הוגה השיטה≠ | Pragmatic trial framework: Schwartz & Lellouch (1967); PRECIS tool: Thorpe et al. (2009) | Classical epidemiology tradition; systematized by Brian MacMahon and Thomas Pugh (1960s) |
| סוג≠ | Observational / quasi-experimental evaluation design | Observational, descriptive/analytic epidemiological design |
| מקור מכונן≠ | Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., & Chalkidou, K. (2009). A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464-475. DOI ↗ | Kelsey, J. L., Whittemore, A. S., Evans, A. S., & Thompson, W. D. (1996). Methods in Observational Epidemiology (2nd ed.). Oxford University Press. ISBN: 978-0195080407 |
| כינויים | pragmatic diagnostic screen evaluation, real-world screening evaluation, effectiveness-oriented screening study, PRECIS-guided screening evaluation | prevalence study, cross-sectional survey, transversal study, cross-sectional design |
| קשורות≠ | 5 | 6 |
| תקציר≠ | A pragmatic screening test evaluation assesses the real-world effectiveness of a screening instrument under routine clinical or public-health conditions — rather than the tightly controlled, ideal-participant settings of explanatory studies. It asks whether the screening tool performs adequately in the actual populations and workflows where it will be deployed, prioritising external validity and implementation relevance over maximally controlled internal conditions. | A cross-sectional epidemiological study measures the exposure(s) and outcome(s) of interest simultaneously in a defined population at a single point in time (or over a short period). Because there is no follow-up, it is the most efficient observational design for estimating disease prevalence and for generating hypotheses about associations between risk factors and health outcomes. |
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