Using implementation science to close the gap between the optimal and typical practice of quantitative methods in clinical science.
Quantitative methods remain the fundamental approach for hypothesis testing, but in approaches to data analysis there is substantial evidence of a gap between what is optimal and what is typical. It is clear that diffusion and dissemination alone are not maximally effective at improving data analyti...
| Publicado en: | Journal of Abnormal Psychology Vol. 128; no. 6; pp. 547 - 563 |
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| Autores principales: | , , , , |
| Formato: | journal article |
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American Psychological Association
Aug2019
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=137886614&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 137886614 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0021843X JAP jtl: Journal of Abnormal Psychology issn: 0021843X maglogo: N pubinfo: dt: Aug2019 vid: 128 iid: 6 pid: 34 pub: American Psychological Association artinfo: ui: 137886614 10.1037/abn0000417 ppf: 547 ppct: 16 formats: tig: atl: Using implementation science to close the gap between the optimal and typical practice of quantitative methods in clinical science. aug: au: King, Kevin M. Pullmann, Michael D. Lyon, Aaron R. Dorsey, Shannon Lewis, Cara C. affil: University of Washington Kaiser Permanente Washington Health Research Institute, Seattle, Washington su: Quantitative research Data analysis Scientific method sug: subj: Quantitative research Data analysis Scientific method keyword: implementation science open science quantitative implementation quantitative methods implementation science open science quantitative implementation quantitative methods ab: Quantitative methods remain the fundamental approach for hypothesis testing, but in approaches to data analysis there is substantial evidence of a gap between what is optimal and what is typical. It is clear that diffusion and dissemination alone are not maximally effective at improving data analytic practices in clinical psychological science. Amid declines in quantitative psychology training, and growing demand for advanced quantitative methods, applied researchers are increasingly called upon to conduct and evaluate research using methods in which they lack expertise. This "research-to-practice" gap in which rigorously developed and empirically supported quantitative methods are not applied in practice has received little attention. In this article, we describe how implementation science, which aims to reduce the research-to-practice gap in health care, offers a promising set of methods for closing the gap for quantitative methods. By identifying determinants of practice (i.e., barriers and facilitators of change), implementation strategies can be selected to increase adoption and high-fidelity application of new quantitative methods to improve scientific inferences and policy and practice decisions in clinical psychological science. (PsycINFO Database Record (c) 2019 APA, all rights reserved). pubtype: Academic Journal doctype: journal article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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