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Using Regression Trees to Find the Factors Influencing the Level of Knowledge about Fertility and the Diet That Supports It among People Dancing in Max Dance Studio in Białystok Cover

Using Regression Trees to Find the Factors Influencing the Level of Knowledge about Fertility and the Diet That Supports It among People Dancing in Max Dance Studio in Białystok

Open Access
|Dec 2021

References

  1. Becker, G. F., Passos, E.P., & Moulin, C. C. (2015). Short-term effects of a hypocaloric diet with low glycemic index and low glycemic load on body adiposity, metabolic variables, ghrelin, leptin, and pregnancy rate in over-weight and obese infertile women: a randomized controlled trial. The American Journal of Clinical Nutrition, 102, 1365–137210.3945/ajcn.115.117200
  2. Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth Publishing, CA.
  3. Chawłowska, E., Lipiak, A., Krzysztoszek, J., Krupa, B., & Staszewski, R. (2020). Reproductive Health Literacy and Fertility Awareness Among Polish Female Students. Frontiers in Public Health, 8(499). doi: 10.3389/fpubh.2020.0049910.3389/fpubh.2020.00499
  4. Eurostat, (2021a). Fertility indicators. Last update: 28-06-2021. Retrieved from: https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=demo_find&lang=en
  5. Eurostat, (2021b). Marriage indicators. Last update: 07-06-2021. Retrieved from: https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=demo_nind&lang=en
  6. Ford, E. A., Roman, S. D., Mc Laughlin, E. A., Beckett, E. L., & Sutherland, J. M. (2020). The association between reproductive health smartphone applications and fertility knowledge of Australian women. BMC Womens Health, 20(1), 45. doi: 10.1186/s12905-020-00912-y10.1186/s12905-020-00912-y
  7. Gambineri, A., Laudisio, D., Marocco, C., Radellini, S., Colao, A., & Savastano, S. (2019). Obesity Programs of nutrition, Education, Research and Assessment (OPERA) group. Female infertility: which role for obesity? International Journal of Obesity Supplements, 9(1), 65–72. doi: 10.1038/s41367-019-0009-110.1038/s41367-019-0009-1
  8. Gatnar, E. (2001). Nieparametryczna metoda dyskryminacji i regresji. Warszawa: PWN.
  9. Griffiths, A. G. F., Modinou, I., Heslop, C., Brand, C., Weatherill, A., Baker, K., Hughes, A. E., Lewis, J. et al. (2019). AccessLab: Workshops to broaden access to scientific research. PLOS Biology, 17(5), e3000258. doi: 10.1371/journal.pbio.300025810.1371/journal.pbio.3000258
  10. Hampton, K., & Mazza, D. (2015). Fertility-awareness knowledge, attitudes and practices of women attending general practice. Australian Family Physician, 44(11), 840–845.
  11. Ilic, D. (2010). The Role of the Internet on Patient Knowledge Management, Education, and Decision-Making. Telemedicine and e-Health, 16(6), 664–669.10.1089/tmj.2010.0003
  12. Jahn, R., Müller, O., Nöst, S., & Bozorgmehr, K. (2020). Public-private knowledge transfer and access to medicines: a systematic review and qualitative study of perceptions and roles of scientists involved in HPV vaccine research. Global Health, 16(22). doi: 10.1186/s12992-020-00552-910.1186/s12992-020-00552-9
  13. Koronacki, J., & Ćwik, J. (2005). Statystyczne systemy uczące się. Warszawa: Wydawnictwa Naukowo-Techniczne.
  14. Maliszewska, A. M., Warska, A., Cendrowski, K., & Sawicki, W. (2017). Inflammatory bowel disease and pregnancy. Ginekologia Polska, 88(7), 398–403. doi:10.5603/GP.a2017.007410.5603/GP.a2017.0074
  15. Milewska, A. J., Jankowska, D., Cwalina, U., Citko, D., Więsak, T., Acacio, B., & Milewski, R. (2016). Prediction of infertility treatment outcomes using classification trees. Studies in Logic, Grammar and Rhetoric, 47(60), 7–19. doi: 10.1515/slgr-2016-0043.10.1515/slgr-2016-0043
  16. Mu, Q., Hanson, L., Hoelzle, J., & Fehring, R. J. (2019). Young Women’s Knowledge About Fertility and Their Fertility Health Risk Factors. Journal of Obstetric, Gynecologic & Neonatal Nursing, 48(2), 153–162.10.1016/j.jogn.2018.12.009
  17. Myszkowska-Raciak, J., Gurtatowska, A., Harton, A., & Gajewska, D. (2013). Nutritional knowledge and selected aspects of the diet of pregnant women. Problemy Higieny i Epidemiologii, 94(3), 600–604.
  18. Newton, V. L., Dickson, J., & Hoggart, L. (2020). Young women’s fertility knowledge: partial knowledge and implications for contraceptive risk-taking. BMJ Sexual & Reproductive Health, 46(2), 147–151.10.1136/bmjsrh-2019-200473
  19. O’Hanlon, R., McSweeney, J., & Stabler, S. (2020). Publishing habits and perceptions of open access publishing and public access amongst clinical and research fellows. Journal of the Medical Library Association, 1, 47–58.10.5195/jmla.2020.751
  20. Skoracka, K., Ratajczak, A. E., Rychter, A. M., Dobrowolska, A., & Krela-Kaźmierczak, I. (2021). Female Fertility and the Nutritional Approach: The Most Essential Aspects. Advances in Nutrition, 12(6), 2372–2386. doi: 10.1093/advances/nmab06810.1093/advances/nmab068
  21. Suliga, E., & Głuszek, S. (2019). The relationship between diet, energy balance and fertility in men. International Journal for Vitamin and Nutrition Research, 90(5–6), 514–526.10.1024/0300-9831/a000577
  22. Zawiejska, A., Ożegowska, K., & Wender-Ożegowska, E. (2016). Choroby endokrynologiczne utrudniające zajście w ciążę. Ginekologia po dyplomie, 46(2), 147–151.
DOI: https://doi.org/10.2478/slgr-2021-0035 | Journal eISSN: 2199-6059 | Journal ISSN: 0860-150X
Language: English
Page range: 597 - 608
Published on: Dec 30, 2021
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Related subjects:

© 2021 Adrianna Zańko, Karolina Milewska, Marcin Warpechowski, Robert Milewski, published by University of Białystok
This work is licensed under the Creative Commons Attribution 4.0 License.