The phrase "attrition factor" is correct and usable in written English.
It can be used in contexts related to statistics, business, or human resources, often referring to the rate at which employees leave an organization or the impact of attrition on a process.
Example: "The attrition factor in our department has increased over the past year, leading to challenges in maintaining productivity."
Alternatives: "turnover rate" or "employee attrition rate."
Of the patients lost to follow-up, 54 (47 %) were on ART; there were no differences in the attrition factors (died, lost, and changed care facility) between patients on ART and those not on ART.
These guidelines assist in evaluating study patient participation and attrition; prognostic factor, outcome, and confounding factor measurement; and the conducted statistical analyses using a four-point ordinal scale (yes/partly/no/unsure) [ 32].
This assessment included evaluations of the following six items: study participation, study attrition, prognostic factor measurements, confounding measurements and relevant adjustments, outcome measurements, and analysis.
Study quality was evaluated using the Quality in Prognosis Studies (QUIPS) tool, which considers the following 6 domains of bias: participation, attrition, prognostic factor measurement (SVR-attainment), outcome measurement (all-cause mortality), confounding, and analysis and reporting [ 22].
Four studies showed a low risk of bias for each of the five domains of the QUIPS tool considered, namely inclusion, attrition, prognostic factor measurement, outcome measurement, and analysis and reporting.
Study quality was evaluated using the Quality in Prognosis Studies (QUIPS) tool, which considers six potential domains of bias: inclusion, attrition, prognostic factor measurement, confounders, outcome measurement, and analysis and reporting [ 18].
> -wrap-foot> Of the 31 included studies, 5.7% of the domains, that is, inclusion, attrition, prognostic factor measurement, outcome measurement, confounding, and analysis and reporting as assessed with the QUIPS tool, showed a high risk of bias, 26.1% showed a moderate risk, and 68.2% showed a low risk of bias (Supplementary Appendix 1).
In total, in the 20 included studies, 5.1% of domains, i.e., inclusion, attrition, prognostic factor measurement, outcome measurement, and analysis and reporting as assessed with the QUIPS tool showed a high risk of bias, 25.5% of domains showed a moderate risk of bias, and 69.4% of domains showed a low risk of bias (full QUIPS appraisal forms for each study are available upon request).
Further research is needed to understand the factors causing such large variation in tutor-to-student ratios between nurse training institutions, factors contributing to Kenya's low level of pre-service attrition, and factors affecting Kenya's ability to recruit, hire and deploy these new nurses.
As this was an exploratory examination of aspects contributing to attrition, many factors that can potentially affect attrition behavior were not measured.
