47 research outputs found
Who leads research productivity growth? Guidelines for R&D policy-makers
[EN] This paper evaluates to what extent policy-makers have been able to promote the creation and consolidation of comprehensive research groups that contribute to the implementation of a successful innovation system. Malmquist productivity indices are applied in the case of the Spanish Food Technology Program, finding that a large size and a comprehensive multi-dimensional research output are the key features of the leading groups exhibiting high efficiency and productivity levels. While identifying these groups as benchmarks, we conclude that the financial grants allocated by the program, typically aimed at small-sized and partially oriented research groups, have not succeeded in reorienting them in time so as to overcome their limitations. We suggest that this methodology offers relevant conclusions to policy evaluation methods, helping policy-makers to readapt and reorient policies and their associated means, most notably resource allocation (financial schemes), to better respond to the actual needs of research groups in their search for excellence (micro-level perspective), and to adapt future policy design to the achievement of medium-long term policy objectives (meso and macro-level).Jiménez Saez, F.; Zabala Iturriagagoitia, JM.; Zofio, JL. (2013). Who leads research productivity growth? Guidelines for R&D policy-makers. Scientometrics. 94(1):273-303. doi:10.1007/s11192-012-0763-0S273303941Abbring, J. H., & Heckman, J. J. (2008). Dynamic policy analysis. In L. Mátyás & P. Sevestre (Eds.), The econometrics of panel data (3rd ed., pp. 795–863). Heidelberg: Springer.Acosta Ballesteros, J., & Modrego Rico, A. (2001). 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The development and preliminary psychometric properties of two positive psychology outcome measures for people with dementia: the PPOM and the EID-Q.
Background:
Positive psychology research in dementia care has largely been confined to the qualitative literature because of the lack of robust outcome measures. The aim of this study was to develop positive psychology outcome measures for people with dementia.
Methods:
Two measures were each developed in four stages. Firstly, literature reviews were conducted to identify and operationalise salient positive psychology themes in the qualitative literature and to examine existing measures of positive psychology. Secondly, themes were discussed within a qualitative study to add content validity for identified concepts (n = 17). Thirdly, draft measures were submitted to a panel of experts for feedback (n = 6). Finally, measures were used in a small-scale pilot study (n = 33) to establish psychometric properties.
Results:
Salient positive psychology themes were identified as hope, resilience, a sense of independence and social engagement. Existing measures of hope and resilience were adapted to form the Positive Psychology Outcome Measure (PPOM). Due to the inter-relatedness of independence and engagement for people with dementia, 28 items were developed for a new scale of Engagement and Independence in Dementia Questionnaire (EID-Q) following extensive qualitative work. Both measures demonstrated acceptable internal consistency (α = .849 and α = .907 respectively) and convergent validity.
Conclusions:
Two new positive psychology outcome measures were developed using a robust four-stage procedure. Preliminary psychometric data was adequate and the measures were easy to use, and acceptable for people with dementia
History lessons
Efficiency, SFA, DEA, B16, C00, D24,
The impact of CON regulation on hospital efficiency
Hospital efficiency, Certificate of need, Directional distance function, Structural efficiency,