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This study examines employees' perceptions of the training and development system in a large Federal government agency. Data come from a database built from a survey with over 3800 respondents. The survey is representative of five populations: executives, managers, supervisors, professional/ administrative and technical/clerical support staff. The survey instrument used to measure employee' perceptions of the training and development system consisted of 68 items in three sections. Section I addressed demographic data, Section II addressed respondents' overall satisfaction with training and development and their perceptions of the training and development system's achievement of elements of effective training and development practice, and Section III addressed the content of training and training delivery methods. Sections II and III employed a Likert scale for respondents' rating of indicators of satisfaction with training and development and respondents' perceptions about the effectiveness of the training and development system. This study provides conclusions about the factor structure underlying the indicators in the survey. It describes the relationships among employees' perceptions of the status and effectiveness of the training and development system and their perceptions of the value of training and development. The study also presents recommendations for further study and for training and development practice. ; Ed. D.

Older adults in many developing nations move to care homes for reasons other than ill health, but the institutional routines, paired with the separation from families, may curtail their autonomy and choices, adversely affecting their active ageing prospects. This study introduced a novel social work intervention that targeted individual-level, personnel-level and institutional-level modifications to improve the active ageing prospects of institutionalised older adults. A total of 35 participants (16 males and 19 females) were recruited from seven care homes in Kerala, India. The active ageing status of the participants significantly improved after the intervention (137.77±45.1) compared to that before the intervention (128±44.2). The findings show that structural and personnel changes in the institution, combined with individual and family-level interventions, can improve the active ageing prospects of institutionalised older adults in developing nations, where institutionalisation is not always necessarily a consequence of ill health. Keywords: older adults, care homes, active ageing, play therapy, mental health education

[Context] Data-driven methods play an increasingly important role in the field of astrophysics In the context of large spectroscopic surveys of stars, data-driven methods are key in deducing physical parameters for millions of spectra in a short time. Convolutional neural networks (CNNs) enable us to connect observables (e.g. spectra, stellar magnitudes) to physical properties (atmospheric parameters, chemical abundances, or labels in general). ; [Aims] We test whether it is possible to transfer the labels derived from a high-resolution stellar survey to intermediate-resolution spectra of another survey by using a CNN. ; [Methods] We trained a CNN, adopting stellar atmospheric parameters and chemical abundances from APOGEE DR16 (resolution R = 22 500) data as training set labels. As input, we used parts of the intermediate-resolution RAVE DR6 spectra (R ∼ 7500) overlapping with the APOGEE DR16 data as well as broad-band ALL_WISE and 2MASS photometry, together with Gaia DR2 photometry and parallaxes. ; [Results] We derived precise atmospheric parameters Teff, log(g), and [M/H], along with the chemical abundances of [Fe/H], [α/M], [Mg/Fe], [Si/Fe], [Al/Fe], and [Ni/Fe] for 420 165 RAVE spectra. The precision typically amounts to 60 K in Teff, 0.06 in log(g) and 0.02−0.04 dex for individual chemical abundances. Incorporating photometry and astrometry as additional constraints substantially improves the results in terms of the accuracy and precision of the derived labels, as long as we operate in those parts of the parameter space that are well-covered by the training sample. Scientific validation confirms the robustness of the CNN results. We provide a catalogue of CNN-trained atmospheric parameters and abundances along with their uncertainties for 420 165 stars in the RAVE survey. ; [Conclusions] CNN-based methods provide a powerful way to combine spectroscopic, photometric, and astrometric data without the need to apply any priors in the form of stellar evolutionary models. The developed procedure can extend the scientific output of RAVE spectra beyond DR6 to ongoing and planned surveys such as Gaia RVS, 4MOST, and WEAVE. We call on the community to place a particular collective emphasis and on efforts to create unbiased training samples for such future spectroscopic surveys. ; Funding for RAVE has been provided by: the Leibniz-Institut für Astrophysik Potsdam (AIP); the Australian Astronomical Observatory; the Australian National University; the Australian Research Council; the French National Research Agency (Programme National Cosmology et Galaxies (PNCG) of CNRS/INSU with INP and IN2P3, co-funded by CEA and CNES); the German Research Foundation (SPP 1177 and SFB 881); the European Research Council (ERC-StG 240271 Galactica); the Istituto Nazionale di Astrofisica at Padova; The Johns Hopkins University; the National Science Foundation of the USA (AST-0908326); the W. M. Keck foundation; the Macquarie University; the Netherlands Research School for Astronomy; the Natural Sciences and Engineering Research Council of Canada; the Slovenian Research Agency (research core funding no. P1-0188); the Swiss National Science Foundation; the Science & Technology Facilities Council of the UK; Opticon; Strasbourg Observatory; and the Universities of Basel, Groningen, Heidelberg, and Sydney. TZ acknowledges financial support of the Slovenian Research Agency (research core funding No. P1-0188) and of the ESA project PHOTO2CHEM (C4000127986). FA is grateful for funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 800502. This work has made use of data from the European Space Agency (ESA) mission Gaia (http://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, http://www.cosmos.esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This publication makes use of data products from the Wide-field Infrared Survey Explorer, which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, funded by the National Aeronautics and Space Administration. This publication makes use of data products from the Two Micron All Sky Survey, which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology, funded by the National Aeronautics and Space Administration and the National Science Foundation.