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Journal articles on the topic 'Scale change'

To see the other types of publications on this topic, follow the link: Scale change.

Author: Grafiati

Published: 10 September 2022

Last updated: 18 September 2022

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1

Mugiya, Davison, and Costa Hofisi. "Climate change adaptation challenges confronting small-scale farmers." Environmental Economics 8, no. 1 (April 12, 2017): 57–65. http://dx.doi.org/10.21511/ee.08(1).2017.06.

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Climate change adaptation issues have recently gained attention for the past few years in Zimbabwe. However, little has been done to explore the challenges, associated with climate change in the country. Therefore, this article explores the challenges affecting small-scale farmers in the Zvishavane District of Zimbabwe in coping with climate change vulnerability. The qualitative research methodology encompassing semi-structured interviews was used to collect data from small-scale farmers and other key informants in the study area. The study portrays that small-scale farmers are struggling to cope with climate change due to resource constraints, lack of access to credit and inputs, aid bottlenecks coupled with contradiction of programs among other critical issues.

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2

Střeleček, F., R. Zdeněk, and J. Lososová. "Influence of production change on return to scale." Agricultural Economics (Zemědělská ekonomika) 57, No. 4 (May 4, 2011): 159–68. http://dx.doi.org/10.17221/93/2010-agricecon.

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The paper deals with an assessment of cost efficiency of farms in 2006–2009 based on a sample of farms classified according to the cost/revenue ratio. The analysis of the sample of 101 farms revealed that the return to scale effect is not significant compared to other effects so that the real increase of the production volume may not determine the dynamic of the profit. The massive shift of farms with increasing cost efficiency to the category of the decreased cost efficiency reflects a significant influence of external conditions to the profit/loss of farms. A positive development of prices in 2007 has influenced an increased cost efficiency of the majority of sample farms. In 2008, the increased prices of agricultural inputs intensively influenced the development of the revenue function. The increase of variable costs influenced by increased input prices has wasted reserves resulted from the production use of fixed costs and the return to scale and caused a significant decrease of profit.

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3

Okhremchuk, І. "Modelling of climate change mitigation policies on national scale." Bìoresursi ì prirodokoristuvannâ 9, no. 3-4 (September 28, 2017): 34–39. http://dx.doi.org/10.31548/bio2017.03.005.

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4

Litzinger, William, Allan M. Mohrman, Susan Albers Mohrman, Gerald E. Ledford, Thomas G. Cummings, and Edward E. Lawler. "Large-Scale Organizational Change." Academy of Management Review 15, no. 4 (October 1990): 710. http://dx.doi.org/10.2307/258696.

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5

Markow, Cathie, and Elliott K. Main. "Creating Change at Scale." Obstetrics and Gynecology Clinics of North America 46, no. 2 (June 2019): 317–28. http://dx.doi.org/10.1016/j.ogc.2019.01.014.

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6

Makimoto, Naoki. "OPTIMAL TIME TO INVEST UNDER UNCERTAINTY WITH A SCALE CHANGE." Journal of the Operations Research Society of Japan 51, no. 3 (2008): 225–40. http://dx.doi.org/10.15807/jorsj.51.225.

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7

Nobles, Brittney M., and Steven R. Erickson. "Variations of a Commonly Used Medication Adherence Assessment Scale: Do Changes in Scale Change Structure Results?" Journal of Pharmacy Technology 34, no. 6 (August 30, 2018): 252–58. http://dx.doi.org/10.1177/8755122518796586.

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Background: Medication nonadherence is a major barrier to both patients and health care professionals when trying to manage medical conditions. An appropriate self-report adherence tool would be helpful in determining a patient’s medication adherence. Objectives: To observe variations in scale scores based on modifications to an Original Adherence Scale, with the hypothesis that making modifications to the Original Adherence Scale will create variations in the percentage of adherent patients. Methods: This cross-sectional study utilized mailed surveys to people identified in a prescription claims administrative dataset who had a pharmacy claim for at least 2 antihypertensive medications. One thousand people were equally divided and randomly placed in 1 of 4 groups: Original Adherence Scale Group, Time Reference Scale Group, 4-Point Likert-Type Scale Group, Multiple Medication Scales Group. Each scale underwent assessment of internal reliability using Cronbach’s α. Changes made to the Original 4-item scale included altering the time reference period from 3 months to 7 days, changing response options from Yes/No to a Likert-type scale, and incorporating multiple scales so that the respondent may report on up to 4 different options. Results: There were 437 surveys completed appropriately, yielding a 46.4% response rate. The overall scale scores indicating perfect adherence was 51.8% for the 4-Point Likert-Type Scale Group, 66.5% for the Multiple Medication Scales Group, 68.8% for the Original Adherence Scale Group, and 78.9% for the Time Reference Scale Group. Conclusion: When there are more selection options, a change in time reference, or more medications reported, the amount of adherent patients varied.

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8

Prein, Andreas Franz, Andreas Gobiet, and Heimo Truhetz. "Analysis of uncertainty in large scale climate change projections over Europe." Meteorologische Zeitschrift 20, no. 4 (August 1, 2011): 383–95. http://dx.doi.org/10.1127/0941-2948/2011/0286.

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9

Northridge, Mary E. "Tempo and Scale of Change." American Journal of Public Health 91, no. 8 (August 2001): 1171. http://dx.doi.org/10.2105/ajph.91.8.1171.

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10

Armenakis, Achilles A., Jeremy B. Bernerth, Jennifer P. Pitts, and H. Jack Walker. "Organizational Change Recipients' Beliefs Scale." Journal of Applied Behavioral Science 43, no. 4 (December 2007): 481–505. http://dx.doi.org/10.1177/0021886307303654.

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11

Erlingsson, Gissur Ó., Jörgen Ödalen, and Erik Wångmar. "Understanding large-scale institutional change." Scandinavian Journal of History 40, no. 2 (March 4, 2015): 195–214. http://dx.doi.org/10.1080/03468755.2015.1016551.

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12

Little, Daniel. "Explaining Large-Scale Historical Change." Philosophy of the Social Sciences 30, no. 1 (March 2000): 89–112. http://dx.doi.org/10.1177/004839310003000105.

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13

Arler, Finn. "Ethics of large-scale change." Geografisk Tidsskrift-Danish Journal of Geography 106, no. 2 (January 2006): 131–44. http://dx.doi.org/10.1080/00167223.2006.10649562.

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14

Barczak, Gloria, Charles Smith, and David Wilemon. "Managing large-scale organizational change." Organizational Dynamics 16, no. 1 (September 1987): 23–35. http://dx.doi.org/10.1016/0090-2616(87)90030-1.

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15

Elisabeth Nyström, Monica, Elisabet Höög, Rickard Garvare, Lars Weinehall, and Anneli Ivarsson. "Change and learning strategies in large scale change programs." Journal of Organizational Change Management 26, no. 6 (October 14, 2013): 1020–44. http://dx.doi.org/10.1108/jocm-08-2012-0132.

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16

Sørup, Hjalte Jomo Danielsen, Stylianos Georgiadis, Ida Bülow Gregersen, and Karsten Arnbjerg-Nielsen. "Formulating and testing a method for perturbing precipitation time series to reflect anticipated climatic changes." Hydrology and Earth System Sciences 21, no. 1 (January 20, 2017): 345–55. http://dx.doi.org/10.5194/hess-21-345-2017.

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Abstract. Urban water infrastructure has very long planning horizons, and planning is thus very dependent on reliable estimates of the impacts of climate change. Many urban water systems are designed using time series with a high temporal resolution. To assess the impact of climate change on these systems, similarly high-resolution precipitation time series for future climate are necessary. Climate models cannot at their current resolutions provide these time series at the relevant scales. Known methods for stochastic downscaling of climate change to urban hydrological scales have known shortcomings in constructing realistic climate-changed precipitation time series at the sub-hourly scale. In the present study we present a deterministic methodology to perturb historical precipitation time series at the minute scale to reflect non-linear expectations to climate change. The methodology shows good skill in meeting the expectations to climate change in extremes at the event scale when evaluated at different timescales from the minute to the daily scale. The methodology also shows good skill with respect to representing expected changes of seasonal precipitation. The methodology is very robust against the actual magnitude of the expected changes as well as the direction of the changes (increase or decrease), even for situations where the extremes are increasing for seasons that in general should have a decreasing trend in precipitation. The methodology can provide planners with valuable time series representing future climate that can be used as input to urban hydrological models and give better estimates of climate change impacts on these systems.

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17

Yang, Jinming, Shimei Li, Jingwei Xu, Xiaojie Wang, and Xiaoguang Zhang. "EFFECTS OF CHANGING SCALES ON LANDSCAPE PATTERNS AND SPATIAL MODELING UNDER URBANIZATION." Journal of Environmental Engineering and Landscape Management 28, no. 2 (March 23, 2020): 62–73. http://dx.doi.org/10.3846/jeelm.2020.12081.

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Spatial scale is an eternal topic in landscape pattern related analysis. This paper examined the spatial scale effect of landscape pattern changes and their relationships with urbanization indicators in Qingdao using a series of sampling blocks. The results indicated that, with the increasing block scale, the mean patch density and aggregation within a block decreased, whereas the diversity increased. Furthermore, the expanding scale amplified the mean change ratio of landscape metrics and eliminated local drastic changes and regional variation trends along an urban-to-rural gradient, which would be obvious at a finer block scale. Meanwhile, the adjusted R2 of GWR (Geographically Weighted Regression) models increased with an increasing block size, especially when the block scale changed from 1 km to 5 km. Odd-numbered block scales performed better than even-numbered block scales.

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18

Ito, Masahiro, and Yoshito Tsuchiya. "TIME SCALE FOR MODELING BEACH CHANGE." Coastal Engineering Proceedings 1, no. 20 (January 29, 1986): 88. http://dx.doi.org/10.9753/icce.v20.88.

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A time scale in the similarity of beach change between model and prototype in transitional beach processes from an initial even slope to an equilibrium is developed using a series of small- and large-scale experiments in which the experimental conditions were set up with the scale-model relationship by the authors (1984). The time scale is obtained empirically as a function of experimental scale. Applied the proposed time scale and the scale-model relationship to model experiments, similarity of morphological beach change such as shoreline change and relative breaker point was well reproduced within the allowable range of experimental error. A semi-theoretical time scale is obtained from the continuity equation, the sediment transport rate, and the scale-model relationship of equilibrium beach profile in two-dimensional beach change. The relation between experimental and semi-theoretical time scale is discussed.

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19

Grimm, Eric C. "Vegetation Change on a Grand Scale." Ecology 69, no. 6 (December 1988): 2038–39. http://dx.doi.org/10.2307/1941187.

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20

ITO, Masahiro. "Time scale for modeling beach change." Doboku Gakkai Ronbunshu, no. 423 (1990): 151–60. http://dx.doi.org/10.2208/jscej.1990.423_151.

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21

Dupont, Lydie. "Orbital scale vegetation change in Africa." Quaternary Science Reviews 30, no. 25-26 (December 2011): 3589–602. http://dx.doi.org/10.1016/j.quascirev.2011.09.019.

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22

Stott, Peter A., and Simon F. B. Tett. "Scale-Dependent Detection of Climate Change." Journal of Climate 11, no. 12 (December 1998): 3282–94. http://dx.doi.org/10.1175/1520-0442(1998)011<3282:sddocc>2.0.co;2.

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23

Cambou, Bernard, Marie Chaze, and Fabian Dedecker. "Change of scale in granular materials." European Journal of Mechanics - A/Solids 19, no. 6 (November 2000): 999–1014. http://dx.doi.org/10.1016/s0997-7538(00)01114-1.

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24

Rose, Tom. "Achieving leadership behavior change at scale." Strategic HR Review 17, no. 6 (November 12, 2018): 309–18. http://dx.doi.org/10.1108/shr-07-2018-0050.

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Purpose The growing force of disruptive change is creating whitewater work environments across the profit and not-for-profit world. To help leaders overcome the challenge of achieving improvements in leadership performance, this study aims to outline research and case examples that illustrate a four-point roadmap for improving this performance at scale. Design/methodology/approach The study summarizes new research on leadership and organizational high performance and practices that drive high performance today. It relates these finding to trends evidenced in the work being done by HR organizations and the consulting firms that serve them. It then outlines an evidence-based roadmap for achieving improvements in leadership performance that HR organizations can adopt to achieve improvements in leadership performance. Findings Successful organizations intervene at four leverage points to meet the challenge achieving the shifts in leadership behavior needed for success in today’s permanent whitewater environments. These organizations are focusing on two types of leadership, leveraging two approaches to its development and are leveraging critical enablers that benefit from strong alignments within HR and between HR and their business leader colleagues. Originality/value The study highlights new research finding and research-based models of leadership performance that meet the demands of today’s workplace. It synthesizes a new four-point roadmap to success from trends discovered in recent research on leadership, technology-assisted behavior change and organizational effectiveness, as well as in the example of in high-performing organizations.

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25

Walters, Glenn D., and Matthew D. Geyer. "The PICTS Fear-of-Change Scale." Prison Journal 87, no. 2 (June 2007): 211–26. http://dx.doi.org/10.1177/0032885507303749.

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26

Santovec, Mary Lou. "Large Scale Change Drives Mankato Transformation." Women in Higher Education 20, no. 5 (May 2011): 8–9. http://dx.doi.org/10.1002/whe.10188.

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27

McKenna, Michael C., and Sharon Walpole. "Planning and Evaluating Change at Scale." Educational Researcher 39, no. 6 (August 2010): 478–83. http://dx.doi.org/10.3102/0013189x10378399.

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28

Joyce Covin, Teresa, and Ralph H. Kilmann. "Critical Issues in Large‐scale Change." Journal of Organizational Change Management 1, no. 2 (February 1988): 59–72. http://dx.doi.org/10.1108/eb025600.

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29

Zwiers, Francis W., and Xuebin Zhang. "Toward Regional-Scale Climate Change Detection." Journal of Climate 16, no. 5 (March 2003): 793–97. http://dx.doi.org/10.1175/1520-0442(2003)016<0793:trsccd>2.0.co;2.

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30

Robinson, M. L. "Present State Examination Change Rating Scale." British Journal of Psychiatry 151, no. 1 (July 1987): 129–30. http://dx.doi.org/10.1192/s0007125000215675.

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31

van Bael, Margriet J., and Kristiaan Temst. "Superconductor responds to nano-scale change." Physics World 18, no. 2 (February 2005): 24–25. http://dx.doi.org/10.1088/2058-7058/18/2/35.

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32

Kezar, Adrianna. "Consortial Leadership Toward Large-Scale Change." Change: The Magazine of Higher Learning 48, no. 6 (November 2016): 50–57. http://dx.doi.org/10.1080/00091383.2016.1247583.

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33

Cullen, Kristin L., Brian D. Webster, Bryan D. Edwards, and Phillip W. Braddy. "Measuring Cumulative Workplace Change: Development of the Cumulative Change Scale." Academy of Management Proceedings 2014, no. 1 (January 2014): 16667. http://dx.doi.org/10.5465/ambpp.2014.16667abstract.

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34

Schwarz, Norbert, Barbel Knauper, Hans-J. Hippler, Elisabeth Noelle-Neumann, and Leslie Clark. "Rating Scales: Numeric Values May Change the Meaning of Scale Labels." Public Opinion Quarterly 55, no. 4 (1991): 570. http://dx.doi.org/10.1086/269282.

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35

Amateis, Ralph L., Mahadev Sharma, and Harold E. Burkhart. "Using miniature-scale plantations as experimental tools for assessing sustainability issues." Canadian Journal of Forest Research 33, no. 3 (March 1, 2003): 450–54. http://dx.doi.org/10.1139/x02-163.

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Decisions concerning ecosystem management, forest certification, and sustainable management require stand- and tree-level information that reflects current silvicultural and management practices. Typical forest stands, however, take years to mature making timely data collection, analyses, and reporting difficult. Further, collecting and evaluating certain forest stand information that affects sustainability such as belowground biomass response or response to changing climatic factors is often intractable. One modeling tool that may be useful for supplying future informational needs at the tree and stand level is the use of miniature scale plantations. Data from a miniature scale spacing trial for loblolly pine (Pinus taeda L.) that was established at 1:16 scale to an operational study suggest that important stand characteristics associated with forest productivity develop similarly at the two scales. Once models are formulated that relate the size of trees (spatially scaled models) and the rates of growth (temporally scaled models) grown at miniature scale to their operational scale counterparts, it may become feasible to conduct experimentation in miniature and make inferences to operational scales.

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36

Stewart, Alan E. "Psychometric Properties of the Climate Change Worry Scale." International Journal of Environmental Research and Public Health 18, no. 2 (January 9, 2021): 494. http://dx.doi.org/10.3390/ijerph18020494.

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Climate change worry involves primarily verbal-linguistic thoughts about the changes that may occur in the climate system and the possible effects of these changes. Such worry is one of several possible psychological responses (e.g., fear, anxiety, depression, and trauma) to climate change. Within this article, the psychometric development of the ten-item Climate Change Worry Scale (CCWS) is detailed in three studies. The scale was developed to assess proximal worry about climate change rather than social or global impacts. Study 1 provided evidence that the CCWS items were internally consistent, constituted a single factor, and that the facture structure of the items was invariant for men and women. The results from Study 1 also indicated a good fit with a Rasch model of the items. Study 2 affirmed the internal consistency of the CCWS items and indicated that peoples’ responses to the measure were temporally stable over a two-week test–retest interval (r = 0.91). Study 3 provided support for the convergent and divergent validity of the CCWS through its pattern of correlations with several established clinical and weather-related measures. The limitations of the studies and the possible uses of the CCWS were discussed. The current work represents a starting point.

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37

Stewart, Alan E. "Psychometric Properties of the Climate Change Worry Scale." International Journal of Environmental Research and Public Health 18, no. 2 (January 9, 2021): 494. http://dx.doi.org/10.3390/ijerph18020494.

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Climate change worry involves primarily verbal-linguistic thoughts about the changes that may occur in the climate system and the possible effects of these changes. Such worry is one of several possible psychological responses (e.g., fear, anxiety, depression, and trauma) to climate change. Within this article, the psychometric development of the ten-item Climate Change Worry Scale (CCWS) is detailed in three studies. The scale was developed to assess proximal worry about climate change rather than social or global impacts. Study 1 provided evidence that the CCWS items were internally consistent, constituted a single factor, and that the facture structure of the items was invariant for men and women. The results from Study 1 also indicated a good fit with a Rasch model of the items. Study 2 affirmed the internal consistency of the CCWS items and indicated that peoples’ responses to the measure were temporally stable over a two-week test–retest interval (r = 0.91). Study 3 provided support for the convergent and divergent validity of the CCWS through its pattern of correlations with several established clinical and weather-related measures. The limitations of the studies and the possible uses of the CCWS were discussed. The current work represents a starting point.

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38

Coomans, Marijke, Martin Taphoorn, Neil Aaronson, Brigitta Baumert, Martin van den Bent, Andrew Bottomley, Alba Brandes, et al. "QOLP-03. MEASURING CHANGE IN HEALTH-RELATED QUALITY OF LIFE: THE ADDED VALUE OF ANALYSIS ON THE INDIVIDUAL PATIENT LEVEL IN GLIOMA PATIENTS IN CLINICAL DECISION MAKING." Neuro-Oncology 21, Supplement_6 (November 2019): vi197—vi198. http://dx.doi.org/10.1093/neuonc/noz175.823.

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Abstract BACKGROUND: Health-related quality of life (HRQoL) is an important outcome in glioma research, reflecting the impact of disease and treatment on a patient’s functioning and wellbeing. Data on changes in HRQoL scores provide important information for clinical decision-making, but different analytical methods may lead to different interpretations of the impact of treatment on HRQoL. This study aimed to study whether different methods to evaluate change in HRQoL result in different interpretations. Methods: HRQoL and sociodemographical/clinical data from 15 randomized clinical trials were combined. Change in HRQoL scores was analyzed: (1)at the group level, comparing mean changes in scale/item scores between treatment arms over time, (2)at the patient level per scale/item by calculating the percentage of patients that deteriorated, improved or remained stable on a scale/item per scale/item, and (3)at the individual patient level combining all scales/items. Results: Data were available for 3727 patients. At the group scale/item level (method 1), only the item ‘hair loss’ showed a significant and clinically relevant change (i.e. ≥10 points) over time, whereas change scores on the other scales/items showed a statistically significant change only (all p< .001, range in change score:0.1–6.2). Analyses on the patient level per scale (method 2) indicated that, while a large proportion of patients had stable HRQoL over time (range:27–84%), many patients deteriorated (range:6–43%) or improved (range:8–32%) on a specific scale/item. At the individual patient level (method 3), the majority of patients (86%) showed both deterioration and improvement, while only 1% of the patients remained stable on all scales. Conclusion: Different analytical methods of changes in HRQoL result in distinct interpretations of treatment effects, all of which may be relevant for clinical decision-making. Additional information about the joint impact of treatment on all outcomes may help patients and physicians to make the best treatment decision.

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39

Itoh, K., and M. Sasai. "Mode Coupling in Large Scale Conformational Change." Seibutsu Butsuri 43, supplement (2003): S55. http://dx.doi.org/10.2142/biophys.43.s55_2.

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40

Callan, Scott J. "Productivity, Scale Economies and Technical Change: Reconsidered." Southern Economic Journal 54, no. 3 (January 1988): 715. http://dx.doi.org/10.2307/1059014.

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41

Wu, Y. P., Y. Y. Hu, H. X. Cao, C. F. Fu, and G. L. Feng. "Computing entropy change in synoptic-scale system." Physica A: Statistical Mechanics and its Applications 494 (March 2018): 163–68. http://dx.doi.org/10.1016/j.physa.2017.12.010.

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42

Terry, Deborah J., and Victor J. Callan. "Employee Adjustment to Large-Scale Organisational Change." Australian Psychologist 32, no. 3 (November 1997): 203–10. http://dx.doi.org/10.1080/00050069708257382.

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43

Trubowitz, Sidney. "Predictable Problems in Achieving Large-Scale Change." Phi Delta Kappan 82, no. 2 (October 2000): 166–68. http://dx.doi.org/10.1177/003172170008200216.

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44

Habibi, Reza. "Change point detection in scale family distributions." Statistica Neerlandica 63, no. 3 (August 2009): 347–52. http://dx.doi.org/10.1111/j.1467-9574.2009.00427.x.

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45

Williams, Ruth. "Leading large-scale change in healthcare settings." Nursing Management 24, no. 7 (October 30, 2017): 17. http://dx.doi.org/10.7748/nm.24.7.17.s17.

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46

Lathia, Neal, Veljko Pejovic, Kiran K. Rachuri, Cecilia Mascolo, Mirco Musolesi, and Peter J. Rentfrow. "Smartphones for Large-Scale Behavior Change Interventions." IEEE Pervasive Computing 12, no. 3 (July 2013): 66–73. http://dx.doi.org/10.1109/mprv.2013.56.

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47

Tress, K. H., C. Bellenis, J. M. Brownlow, G. Livingston, and J. P. Leff. "The Present State Examination Change Rating Scale." British Journal of Psychiatry 150, no. 2 (February 1987): 201–7. http://dx.doi.org/10.1192/bjp.150.2.201.

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The development and use of a new psychiatric symptom change scale based on the Present State Examination (PSE) - the PSE Change Rating Scale - is described. The advantages of the PSE are retained (the extensive glossary, the diagnostic facility and the standardised interview technique) while improving its sensitivity to change and decreasing its administration time. The scale has been designed to prevent feedback of results and ‘halo’ effects: it has proved highly reliable in use across raters with different backgrounds and experience. The scale has been used to monitor drug effects on clinical state across a variety of diagnoses and examples of its use are given.

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48

Kennedy, Ruth, Melanie Lawless, and Beverley Slater. "The ten essentials of large-scale change." British Journal of Healthcare Management 15, no. 12 (December 2009): 580–85. http://dx.doi.org/10.12968/bjhc.2009.15.12.45637.

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49

Buera, Francisco J., and Joseph P. Kaboski. "Scale and the origins of structural change." Journal of Economic Theory 147, no. 2 (March 2012): 684–712. http://dx.doi.org/10.1016/j.jet.2010.11.007.

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50

Rossler, Otto E., and Heinrich Kuypers. "The scale change of Einstein’s equivalence principle." Chaos, Solitons & Fractals 25, no. 4 (August 2005): 897–99. http://dx.doi.org/10.1016/j.chaos.2004.11.097.

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