Journal articles on the topic 'Demographics'

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1

Jackson, R. W., and M. Hynes. "Demographics in Demographic—Economic Models: A Reply to Madden." Environment and Planning A: Economy and Space 20, no. 11 (November 1988): 1543–45. http://dx.doi.org/10.1068/a201543.

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2

Goldberg, Mark F. "Demographics —." Phi Delta Kappan 82, no. 4 (December 2000): 304–6. http://dx.doi.org/10.1177/003172170008200413.

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3

Gillis, Artha J., and Eraka Bath. "Demographics." Child and Adolescent Psychiatric Clinics of North America 25, no. 1 (January 2016): 1–17. http://dx.doi.org/10.1016/j.chc.2015.08.001.

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4

Hokenson, Richard F. "The Demographics." AIMR Conference Proceedings 1995, no. 6 (October 1995): 5–13. http://dx.doi.org/10.2469/cp.v1995.n6.2.

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5

Schrad, Mark Lawrence. "Abnormal Demographics." Foreign Affairs 83, no. 4 (2004): 150. http://dx.doi.org/10.2307/20034057.

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6

Contrino, Heather, and Nancy McGuckin. "Demographics Matter." Public Works Management & Policy 13, no. 4 (April 2009): 361–68. http://dx.doi.org/10.1177/1087724x09336223.

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7

Williams, Betty Fry. "Changing Demographics." Intervention in School and Clinic 27, no. 3 (January 1992): 157–63. http://dx.doi.org/10.1177/105345129202700306.

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8

Reibel, Michael, Cathy Olmo, Sandy Andrada, and Jeff Koertzen. "Deep Demographics." Progress in Transplantation 26, no. 2 (April 4, 2016): 191–98. http://dx.doi.org/10.1177/1526924816640670.

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9

RICH, V. "Russian demographics." Lancet 344, no. 8919 (August 1994): 399. http://dx.doi.org/10.1016/s0140-6736(94)91420-6.

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10

Fuster, Valentin. "Changing Demographics." Journal of the American College of Cardiology 69, no. 24 (June 2017): 3002–5. http://dx.doi.org/10.1016/j.jacc.2017.05.013.

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11

Buss, Daryl D. "Changing demographics." Journal of the American Veterinary Medical Association 217, no. 3 (August 2000): 327–28. http://dx.doi.org/10.2460/javma.2000.217.327.

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12

Alexander, G. Rumay, and James H. Johnson. "Disruptive Demographics." Nursing Administration Quarterly 45, no. 1 (January 2021): 58–64. http://dx.doi.org/10.1097/naq.0000000000000449.

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13

Robertson, Rachel E., Emily E. Sobeck, Kaylee Wynkoop, and Rachel Schwartz. "Participant Diversity in Special Education Research: Parent-Implemented Behavior Interventions for Children With Autism." Remedial and Special Education 38, no. 5 (January 11, 2017): 259–71. http://dx.doi.org/10.1177/0741932516685407.

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Evidence-based practices for children with disabilities have empirical support for their effectiveness; however, the demographics of participants making up the evidence base are generally unknown, leaving generalizability of the evidence unclear. This study presents a systematic literature review of participant racial and socioeconomic demographics in one area of special education research, parent-implemented interventions for reducing problem behavior in children with autism, to examine demographic reporting practices, participant characteristics, and their similarity to the general population. Participant race, income, education level, and marital status were aggregated across 23 studies and compared with population-level demographics using chi-square analyses. Results indicated (a) these demographics were infrequently reported; (b) participants with reported demographics were overwhelmingly from White, well-educated, two-parent families; and (c) participants were significantly different from the U.S. population on every tested demographic. Implications of findings and recommendations for reporting participant demographics and increasing diversity in special education research are discussed.
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14

Madden, M. "Demographics in Demographic—Economic Models: Notes on Two Activity—Commodity Frameworks." Environment and Planning A: Economy and Space 20, no. 11 (November 1988): 1537–42. http://dx.doi.org/10.1068/a201537.

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15

Weidner, Amanda, and Deborah S. Clements. "CAFM LEADERSHIP DEMOGRAPHICS." Annals of Family Medicine 19, no. 2 (March 2021): 181–85. http://dx.doi.org/10.1370/afm.2678.

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16

Knight, Michael B., and J. Michael Pearson. "The Changing Demographics." Journal of Organizational and End User Computing 17, no. 4 (October 2005): 49–65. http://dx.doi.org/10.4018/joeuc.2005100103.

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17

Aquavella, James V. "Technology and demographics." Expert Review of Ophthalmology 6, no. 1 (February 2011): 5–6. http://dx.doi.org/10.1586/eop.10.88.

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18

Edwards, Jennifer M., and Roland A. Patry. "Pharmacy’s changing demographics." American Journal of Health-System Pharmacy 63, no. 18 (September 15, 2006): 1691. http://dx.doi.org/10.2146/ajhp060340.

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19

Beasley, Craig J. "Our changing demographics." Leading Edge 24, no. 7 (July 2005): 679–80. http://dx.doi.org/10.1190/tle24070679.1.

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20

Ash, C. "MICROBIOLOGY: Intracellular Demographics." Science 314, no. 5800 (November 3, 2006): 727c. http://dx.doi.org/10.1126/science.314.5800.727c.

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21

Rosen, Anita L., and Joan Levy Zlotnik. "Demographics and Reality." Journal of Gerontological Social Work 36, no. 3-4 (August 8, 2002): 81–97. http://dx.doi.org/10.1300/j083v36n03_07.

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22

Choucri, Nazli. "Demographics and conflict." Bulletin of the Atomic Scientists 42, no. 4 (April 1986): 24–25. http://dx.doi.org/10.1080/00963402.1986.11459353.

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23

Feyrer, James. "Demographics and Productivity." Review of Economics and Statistics 89, no. 1 (February 2007): 100–109. http://dx.doi.org/10.1162/rest.89.1.100.

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24

Liang, James, Hui Wang, and Edward P. Lazear. "Demographics and Entrepreneurship." Journal of Political Economy 126, S1 (October 2018): S140—S196. http://dx.doi.org/10.1086/698750.

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25

Pjesivac, Ivanka. "Do Demographics Matter?" Journalism Practice 11, no. 5 (May 3, 2016): 606–24. http://dx.doi.org/10.1080/17512786.2016.1172943.

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26

Kuusisto, Finn. "Demographics in computing." XRDS: Crossroads, The ACM Magazine for Students 20, no. 4 (June 2014): 63. http://dx.doi.org/10.1145/2627812.

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27

Raccoon, L. B. S. "Definitions and demographics." ACM SIGSOFT Software Engineering Notes 26, no. 1 (January 2001): 82–91. http://dx.doi.org/10.1145/505894.505914.

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28

HEYLIN, MICHAEL. "DEMOGRAPHICS OF CHEMISTRY." Chemical & Engineering News 85, no. 38 (September 17, 2007): 43–44. http://dx.doi.org/10.1021/cen-v085n038.p043.

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29

Goldner, Richard D., Robert D. Fitch, James A. Nunley, Martha S. Aitken, and James R. Urbaniak. "Demographics and replantation." Journal of Hand Surgery 12, no. 5 (September 1987): 961–65. http://dx.doi.org/10.1016/s0363-5023(87)80265-4.

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30

Ledger, William L. "Demographics of infertility." Reproductive BioMedicine Online 18 (January 2009): S11—S14. http://dx.doi.org/10.1016/s1472-6483(10)60442-7.

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31

Zillén, Per Åke, and Maria Mindak. "World Dental Demographics." International Dental Journal 50, no. 4 (August 2000): 194–97. http://dx.doi.org/10.1111/j.1875-595x.2000.tb00558.x.

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32

Carbone, Enrica. "Demographics and Behaviour." Experimental Economics 8, no. 3 (September 2005): 217–32. http://dx.doi.org/10.1007/s10683-005-1464-9.

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33

Backer, Elisa, and Brian King. "VFR traveller demographics." Journal of Vacation Marketing 23, no. 3 (August 29, 2016): 191–204. http://dx.doi.org/10.1177/1356766716665439.

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The global phenomenon of visiting friends and relatives (VFR) travel is substantial, including in developed countries. In profiling its dimensions, researchers have examined various VFR characteristics including length of stay, origins and travel mode, though no thorough exploration has been undertaken of VFR demographics. Surprisingly this is the first large-scale study to examine VFR demographics and to consider all destination-related VFR data, not only those that are confined to purpose of visit. The researchers drew upon official Australian domestic travel data ( n = 81,579) to undertake a profile of the respective demographics of VFRs and non-VFRs. This assessment revealed that many people who would be classified as socio-economically disadvantaged engage in VFR travel. Previous literature has documented the lower participation of socio-economically disadvantaged groups in tourism and this article shows that they participate actively as VFRs. It is concluded that stimulating VFR travel provision may provide a mechanism for extending leisure experiences to those excluded from other forms of tourism.
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34

McKellips, Paul. "Dollars and demographics." Lab Animal 43, no. 1 (December 19, 2013): 35. http://dx.doi.org/10.1038/laban.440.

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35

Baumstein, Carol, Susan Block, and Jennifer Whitworth. "Managing Patient Demographics." Journal For Healthcare Quality 16, no. 1 (January 1994): 18–21. http://dx.doi.org/10.1111/j.1945-1474.1994.tb00682.x.

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36

Noble, J. Kendrick. "Demographics and destiny." Book Research Quarterly 4, no. 4 (December 1988): 32–37. http://dx.doi.org/10.1007/bf02683769.

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37

Nicolosi, Gina. "Demographics of dividends." Journal of Corporate Finance 23 (December 2013): 54–70. http://dx.doi.org/10.1016/j.jcorpfin.2013.07.003.

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38

Ding, Yi, Jacob You, Tonja-Katrin Machulla, Jennifer Jacobs, Pradeep Sen, and Tobias Höllerer. "Impact of Annotator Demographics on Sentiment Dataset Labeling." Proceedings of the ACM on Human-Computer Interaction 6, CSCW2 (November 7, 2022): 1–22. http://dx.doi.org/10.1145/3555632.

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As machine learning methods become more powerful and capture more nuances of human behavior, biases in the dataset can shape what the model learns and is evaluated on. This paper explores and attempts to quantify the uncertainties and biases due to annotator demographics when creating sentiment analysis datasets. We ask >1000 crowdworkers to provide their demographic information and annotations for multimodal sentiment data and its component modalities. We show that demographic differences among annotators impute a significant effect on their ratings, and that these effects also occur in each component modality. We compare predictions of different state-of-the-art multimodal machine learning algorithms against annotations provided by different demographic groups, and find that changing annotator demographics can cause >4.5 in accuracy difference when determining positive versus negative sentiment. Our findings underscore the importance of accounting for crowdworker attributes, such as demographics, when building datasets, evaluating algorithms, and interpreting results for sentiment analysis.
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39

Schrock, Denny, Mary Meyer, Peter Ascher, and Mark Snyder. "142 Missouri Master Gardener Demographics." HortScience 34, no. 3 (June 1999): 466B—466. http://dx.doi.org/10.21273/hortsci.34.3.466b.

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A survey was conducted of current and former Missouri Master Gardeners to identify the demographics of volunteers and to determine if Master Gardeners fit the demographic pattern of volunteers in general. Sixty-eight percent of survey respondents were active in the program, while 32% were inactive. Females accounted for 65% of respondents and males 35%. Nearly 60% of Missouri Master Gardeners were 50 years old or older; however, those in their 40s comprised the largest demographic group. The majority of Missouri Master Gardeners were married with children. Over 50% had at least a college degree, while 22% had post-graduate work. One-third had household incomes of $60,000 or greater; in addition, just under one-quarter had household incomes between $40,000 and $60,000. The largest occupational group was retired persons, at 26.9%; the second largest category was homemakers at 14.6%. Missouri Master Gardeners are more likely to be from small towns or rural areas than from medium or large cities. They tend to be long-term residents of their communities; 57.2% had lived at their current residence for more than 10 years. Missouri Master Gardener volunteer demographics fit the pattern of volunteers in general, but demographic data proved to be a poor predictor of intent to continue volunteering in the Master Gardener program.
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40

Hynes, M., and R. W. Jackson. "Demographics in Demographic—Economic Models: A Note on the Basic Activity—Commodity Framework." Environment and Planning A: Economy and Space 20, no. 11 (November 1988): 1531–36. http://dx.doi.org/10.1068/a201531.

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The significant contributions of demographic—economic modelling to the area of impact assessment are recognised, but it is argued that much greater attention must be focused on the demographic components of these models. In particular, distinctions between household types must be treated more adequately, and mechanisms for changes in household type must be articulated and refined. The inadequacy of the current state of demographic—economic models in this regard is demonstrated by a reconsideration of the early basic formulation of Madden and Batey, upon which much of the subsequent literature draws. The simple failure of this model to allocate new jobs to unemployed nonheads of households illustrates the need for increased focus on the demographic aspect of demo—economic models.
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41

Nazaruk, M., and Y. Zhuk. "Social and environmental processes and their impact of demographic situation in the small cities of Lviv region." Visnyk of the Lviv University. Series Geography, no. 45 (May 20, 2014): 200–209. http://dx.doi.org/10.30970/vgg.2014.45.1166.

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The article describes the socio-ecological situation in the small cities of Lviv region, analyzed population dynamics of small towns in the historical period. Done mapped schemes typology of small towns by demographics. Considered the influence of social and environmental situation in the demographic situation in small towns. Key words: small town, demographics, social and environmental problems.
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42

Regnante, Jeanne M., Barbara Bierer, and Karen Winkfield. "Abstract A024: Emerging editorial policies, researcher and author standards in promoting diversity, inclusion, and equity in research: A focus on cancer." Cancer Epidemiology, Biomarkers & Prevention 32, no. 1_Supplement (January 1, 2023): A024. http://dx.doi.org/10.1158/1538-7755.disp22-a024.

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Abstract By modernizing cancer journal roles and editorial policy there may be greater rigor, biological understanding, scientific generalizability, reproducibility, transparency, and inclusion in cancer research. A path to identifying, addressing and correcting of structural inequities in health has been recently established by three major medical journal’s editorial policies to date and continue to be influenced by the reality of several reviews of publications over years and decades of clinical trials in cancer where specifically race and ethnicity demographics of study populations have been under-reported. The NIH has acknowledged the importance of demographic reporting and the FDA has established that data reporting in clinical trials is important. Question: What is needed to transform the ecosystem to ensure standards in promoting diversity, inclusion, and equity in cancer research? There are several considerations for stakeholders in organizations to address the issue of demographic reporting in research which includes both gender and race. There are metrics intrinsic to journal leadership which may include (i) demographics and diversity of leadership and editorial boards (ii) demographics and diversity of research staff. There are author considerations such as (i) diverse representation of authors (ii) Inclusion of a citation diversity statement, with attention to diversity in referenced citations (iii) statement of efforts and policy made to provide for inclusive environment, mentorship, and participation. Specifically, to ensure diversity and inclusion in non-therapeutic and therapeutic research, demographic and non-demographic variables in basic, clinical, and outcomes research should be considered by investigators and statisticians. For example, a statement of US and/or ex-US demographics of the disease, to whom the research question applies, and for whom the intervention is intended, and eligibility requirements that are scientifically justified. ASCO, AACR, MRCT- Harvard recommend action and policies for publicly reporting robust demographic characteristics on the enrolled clinical trial sample as part of the future modernization of journal editorial policy. The issue of demographic collection and reporting in research may be influenced by a variety of factors which include journal requirements, author demographics, investigator gender and race standards, bias, actual under-representation of the study population in the research consistent with the disease under study or rigor in the collection of demographics of the study population. There is a need for greater transparency of researcher, author standards, and journal requirements, leading to transparent research participant demographic reporting consistent with the research question. In all cases, the ability for any researcher, health care advocate or student to fully understanding the next question that needs to be answered and the demographics of study participants for inclusion is not fully optimized. Citation Format: Jeanne M. Regnante, Barbara Bierer, Karen Winkfield. Emerging editorial policies, researcher and author standards in promoting diversity, inclusion, and equity in research: A focus on cancer [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr A024.
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43

Jakoš, Aleksander. "Planning, demographics and Slovenia." Urbani izziv 20, no. 1 (2009): 141–52. http://dx.doi.org/10.5379/urbani-izziv-en-2009-20-01-002.

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44

Gillis, Anna Maria. "Sex Selection and Demographics." BioScience 45, no. 6 (June 1995): 384–85. http://dx.doi.org/10.2307/1312716.

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45

Kalia, Prateek. "Product Category vs Demographics." International Journal of E-Adoption 10, no. 2 (July 2018): 20–36. http://dx.doi.org/10.4018/ijea.2018070102.

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To remain profitable, managers and researchers want to gain insights about products bought by e-shoppers in past and their future shopping interests. They also want to know, “what factors are creating difference in shopping behavior of these buyers.” This article addresses above situation by presenting product category-wise demographic comparison of past and future e-purchase intentions of e-shoppers. Results revealed significant differences in past e-purchases within gender, marital status, age, city of residence and occupational categories with respect to different product categories, surprisingly no such differences were observed in educational and family income categories. For future e-purchases intentions, significant differences were found within gender, city of residence, marital status, age and education categories. Here differences within occupational and family income groups were not observed. Maximum demographic differences were observed in product categories like clothing, books and auto parts.
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46

DIAZ, ANA, and ERIN MCGRATH. "Demographics of the Profession." Political Science Today 2, no. 2 (May 2022): 7. http://dx.doi.org/10.1017/psj.2022.25.

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47

Manser, Lynda. "Canadian military family demographics." Journal of Military, Veteran and Family Health 6, no. 1 (May 1, 2020): 9–12. http://dx.doi.org/10.3138/jmvfh-2019-0003.

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48

Read, Marsha H., Karen A. Fisher, Robert Bendel, V. J. Bhalla, M. Ann Bock, Inez Harrill, Madeleine Mitchell, Howard Schutz, Ed Sheehan, and B. Standal. "Dietary fat intake: Demographics." Journal of the American Dietetic Association 89, no. 6 (June 1989): 830–31. http://dx.doi.org/10.1016/s0002-8223(21)02258-6.

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49

McGRATH, ERIN. "Demographics of the Profession." Political Science Today 1, no. 2 (May 2021): 40–41. http://dx.doi.org/10.1017/psj.2021.39.

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50

Bloom, David E., and David Canning. "Demographics And Development Policy." Development Outreach 13, no. 1 (April 2011): 77–81. http://dx.doi.org/10.1596/1020-797x_13_1_77.

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