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Journal articles on the topic 'Couns'

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Author: Grafiati

Published: 26 July 2024

Last updated: 28 July 2024

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1

Lee, Jaewon, Wan-Sik Seo, Ye-Soo Park, and Young-Ha Oh. "Sequestrated Intradural Disc Herniation Around Couns Medullaris: A Case Report." Journal of Korean Society of Spine Surgery 21, no. 3 (2014): 134. http://dx.doi.org/10.4184/jkss.2014.21.3.134.

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2

Ridsdale, L., M. Hurley, M. King, P. McCrone, and N. Donaldson. "The effect of counselling, graded exercise and usual care for people with chronic fatigue in primary care: a randomized trial." Psychological Medicine 42, no. 10 (February 28, 2012): 2217–24. http://dx.doi.org/10.1017/s0033291712000256.

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BackgroundTo evaluate the effectiveness of graded exercise therapy (GET), counselling (COUNS) and usual care plus a cognitive behaviour therapy (CBT) booklet (BUC) for people presenting with chronic fatigue in primary care.MethodA randomized controlled trial in general practice. The main outcome measure was the change in the Chalder fatigue score between baseline and 6 months. Secondary outcomes included a measure of global outcome, including anxiety and depression, functional impairment and satisfaction.ResultsThe reduction in mean Chalder fatigue score at 6 months was 8.1 [95% confidence interval (CI) 6.6–10.4] for BUC, 10.1 (95% CI 7.5–12.6) for GET and 8.6 (95% CI 6.5–10.8) for COUNS. There were no significant differences in change scores between the three groups at the 6- or 12-month assessment. Dissatisfaction with care was high. In relation to the BUC group, the odds of dissatisfaction at the 12-month assessment were less for the GET [odds ratio (OR) 0.11, 95% CI 0.02–0.54, p=0.01] and COUNS groups (OR 0.13, 95% CI 0.03–0.53, p=0.004).ConclusionsOur evidence suggests that fatigue presented to general practitioners (GPs) tends to remit over 6 months to a greater extent than found previously. Compared to BUC, those treated with graded exercise or counselling therapies were not significantly better with respect to the primary fatigue outcome, although they were less dissatisfied at 1 year. This evidence is generalizable nationally and internationally. We suggest that GPs ask patients to return at 6 months if their fatigue does not remit, when therapy options can be discussed further.

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3

Egenberg, Herman, Jennifer Gerwing, Hanne C. Lie, Pål Gulbrandsen, and Julia Menichetti. "Withdrawal notice to “If I say…teach-back” [Patient Educ Couns 116 (2023) 107982]." Patient Education and Counseling 116 (November 2023): 107991. http://dx.doi.org/10.1016/j.pec.2023.107991.

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4

Little, Elizabeth, and Lea Simpson. "Counts Count." Art Documentation: Journal of the Art Libraries Society of North America 42, no. 1 (March 1, 2023): 24–35. http://dx.doi.org/10.1086/728258.

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5

Croxford, Leigh Ann. "Patient Education Objectives in Healthy People 2000: Policy and Research Issues. Tolsma D. Patient Educ Couns 22:7-14, 1993." Journal of Physical Therapy Education 8, no. 2 (1994): 81. http://dx.doi.org/10.1097/00001416-199407000-00013.

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6

Newman, Sue NM. "A Low-literacy Skin Care Manual for Spinal Cord Injury Patients. Yasenchak PA, Bridle MJ. Patient Educ Couns 22:1-5, 1993." Journal of Physical Therapy Education 8, no. 2 (1994): 81–82. http://dx.doi.org/10.1097/00001416-199407000-00014.

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7

Liang, Chaocong, Aijing Luo, and Zhuqing Zhong. "Knowledge mapping of medication literacy study: A visualized analysis using CiteSpace." SAGE Open Medicine 6 (January 2018): 205031211880019. http://dx.doi.org/10.1177/2050312118800199.

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Objective: The objective of this study was to analyze knowledge mapping and demonstrate the status quo, intellectual base, and hotspots in the field of medication literacy. Methods: Using the data from Web of Science Core database, we constructed a knowledge map to visualize medication literacy using CiteSpace, which revealed the power of the studies, core authors and journals, intellectual base, and hotspots in this field. Results: According to an analysis of 2025 literature reports, the stronger studies were mainly conducted at research institutions of higher education in the United States. Core author groups with a higher influence were not identified. The core journals included Patient Educ Couns and Fam Med. The health literacy studies served as the foundation for the medication literacy studies. The keywords formed 13 clusters including 5 major clusters. Conclusion: The topics in medication literacy study focused on instruments assessing medication literacy, measurement and assessment of medication literacy, medication literacy for the prevention of chronic disease and medication treatment adherence, medication literacy education, and family practice. This study provides an insight into medication literacy and valuable information for medication literacy researchers to identify new perspectives on potential collaborators and cooperative institutions and hotspots.

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8

Hickman, Robbin. "Patient Education and Health Promotion: Clinical Health Promotion—the Conceptual Link. CaraherM (Thames Valley University, London, UK). Patient Educ Couns. 1998;33:49-58." Journal of Physical Therapy Education 15, no. 3 (2001): 76. http://dx.doi.org/10.1097/00001416-200107000-00018.

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9

Cameron, Kenzie A. "Corrigendum to “A practitioner's guide to persuasion: An overview of 15 selected persuasion theories, models and frameworks” [Patient Educ Couns 2009;74:309–317]." Patient Education and Counseling 76, no. 2 (August 2009): 291. http://dx.doi.org/10.1016/j.pec.2009.07.004.

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10

Österlund Efraimsson, Eva, Birgitta Klang, Kjell Larsson, Anna Ehrenberg, and Bjöörn Fossum. "Erratum to “Communication and self-management education at nurse-led COPD clinics in primary health care” [Patient Educ Couns 77 (2009) 209–217]." Patient Education and Counseling 79, no. 2 (May 2010): 272. http://dx.doi.org/10.1016/j.pec.2010.03.008.

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11

Wagner, Gregory R. "Work Counts So Count It." Respiration 84, no. 3 (2012): 191–92. http://dx.doi.org/10.1159/000339419.

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12

Lo, Selina, and Richard Horton. "Everyone counts—so count everyone." Lancet 386, no. 10001 (October 2015): 1313–14. http://dx.doi.org/10.1016/s0140-6736(15)60305-1.

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13

Ho, D. D. "Viral Counts Count in HIV Infection." Science 272, no. 5265 (May 24, 1996): 1124–25. http://dx.doi.org/10.1126/science.272.5265.1124.

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14

Billard, Lynne. "The Census Count: Who Counts? How Do We Count? When Do We Count?" PS: Political Science & Politics 33, no. 04 (December 2000): 767–74. http://dx.doi.org/10.1017/s1049096500061977.

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15

Billard, Lynne. "The Census Count: Who Counts? How Do We Count? When Do We Count?" PS: Political Science and Politics 33, no. 4 (December 2000): 767. http://dx.doi.org/10.2307/420913.

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16

Schulz, Peter J., Kent Nakamoto, David Brinberg, and Joachim Haes. "Corrigendum to “More than nation and knowledge: cultural micro-diversity and organ donation in Switzerland” [Patient Educ. Couns. 64, December (1–3) (2006) 294–302]." Patient Education and Counseling 101, no. 11 (November 2018): 2044. http://dx.doi.org/10.1016/j.pec.2018.07.014.

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17

Gagliardi, Anna R., Guy Faulkner, Donna Ciliska, and Audrey Hicks. "Corrigendum to “Factors contributing to the effectiveness of physical activity counselling in primary care: A realist systematic review” [Patient Educ. Couns. 98 (4) (2015) 412–419]." Patient Education and Counseling 98, no. 7 (July 2015): 923. http://dx.doi.org/10.1016/j.pec.2015.04.001.

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18

Graven, L. J., G. Gordon, J. Grant Keltner, L. Abbott, and J. Bahorski. "Corrigendum to “Efficacy of a social support and problem-solving intervention on heart failure self-care: A pilot study” [Patient Educ. Couns. 101 (2018) 266–275]." Patient Education and Counseling 102, no. 8 (August 2019): 1575–76. http://dx.doi.org/10.1016/j.pec.2019.04.017.

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19

Gorman, G. E. "How do we count our chickens? Or do citation counts count?" Online Information Review 29, no. 6 (December 2005): 581–84. http://dx.doi.org/10.1108/14684520510638043.

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20

Kuo, Yu-Hsuan, Cho-Chun Chiu, Daniel Kifer, Michael Hay, and Ashwin Machanavajjhala. "Differentially private hierarchical count-of-counts histograms." Proceedings of the VLDB Endowment 11, no. 11 (July 2018): 1509–21. http://dx.doi.org/10.14778/3236187.3236202.

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21

Kordower, Jeffrey H. "Making the counts count: the stereology revolution." Journal of Chemical Neuroanatomy 20, no. 1 (October 2000): 1–2. http://dx.doi.org/10.1016/s0891-0618(00)00079-x.

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22

Tullus, Kjell. "Low urinary bacterial counts: do they count?" Pediatric Nephrology 31, no. 2 (October 16, 2015): 171–74. http://dx.doi.org/10.1007/s00467-015-3227-y.

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23

Townsend, Christine, and Nancy Skinner. "When Counts Count: Improving Practice and Documentation." Journal of Obstetric, Gynecologic & Neonatal Nursing 39 (September 2010): S82. http://dx.doi.org/10.1111/j.1552-6909.2010.01121_47.x.

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24

Grayson, Lesley. "Making Research Count – where it really counts." Evidence & Policy: A Journal of Research, Debate and Practice 3, no. 1 (January 1, 2007): 135–38. http://dx.doi.org/10.1332/174426407779702157.

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25

Yunia, Yusi Ratna, Sri Dinengsih, and Shinta Novelia. "Analysis of Compliance Factors of Pregnant Mothers to Health Protocol Covid 19 when Containing Antenatal Care." Jurnal Kebidanan Midwiferia 8, no. 1 (April 5, 2022): 91–105. http://dx.doi.org/10.21070/midwiferia.v8i1.1637.

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WHO recommends regular pregnancy checkups 8 meetings. However, with the current condition of the Covid-19 pandemic still not over, routine checks can be reduced in intensity to just 6 times. In several regions in Indonesia, such as in Yogyakarta, 6 pregnant women who tested positive for Covid 19 were found, in Surabaya there were 123 pregnant women who were positive for Covid-19, in Gresik there were 20 positive pregnant women for Covid-19 and in the Lampung region there were also three pregnant women who were positive Covid-19. With the increasing number of pregnant women who are positive for Covid 19, compliance with health protocols must be tightened in which pregnant women are people who are vulnerable to Covid 19 transmission. , and family support. The Objectives: know the analysis of the compliance factor of pregnant women with the Covid 19 health protocol during an ANC visit. Analytical research using cross-sectional method. The sample in this study amounted to 100 respondents. The sampling technique used total sampling. The research instrument was a questionnaire. Data were analyzed using univariate and bivariate with chi square statistical test. There was no significant relationship between age and education (p value> 0.05), while knowledge and family support showed a relationship with covid 19 health protocol compliance with p value <0.05. Compliance with the covid 19 health protocol in pregnant women is influenced by knowledge and family support. It is hoped that health workers should increase couns

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26

Alders, Irène, Inge Henselmans, Carolien Smits, Tommy Visscher, Monique Heijmans, Jany Rademakers, Paul L. P. Brand, and Sandra van Dulmen. "Corrigendum to “Patient coaching in specialist consultations. Which patients are interested in a coach and what communication barriers do they perceive?” [Patient Educ. Couns. 102 (2019) 1520–1527]." Patient Education and Counseling 103, no. 2 (February 2020): 431. http://dx.doi.org/10.1016/j.pec.2019.11.015.

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27

Dale, Jeremy, Harbinder Sandhu, Ranjit Lall, and Edward Glucksman. "Erratum to “The patient, the doctor and the emergency department: A cross-sectional study of patient-centredness in 1990 and 2005” [Patient Educ Couns 72 (2008) 320–329]." Patient Education and Counseling 74, no. 2 (February 2009): 278. http://dx.doi.org/10.1016/j.pec.2008.12.019.

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28

Perez Jolles, Monica, Jennifer Richmond, and Kathleen C. Thomas. "Erratum to “Minority patient preferences, barriers, and facilitators for shared decision-making with health care providers in the USA: A systematic review” [Patient Educ. Couns. 102 (2019) 1251–1262]." Patient Education and Counseling 103, no. 2 (February 2020): 430. http://dx.doi.org/10.1016/j.pec.2019.11.014.

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29

Singh, Neha, and Kalpana Kumari MK. "Correlation of Absolute Lymphocyte Count and CD4 Counts in HIV Infected Patients at a Tertiary Care Institute." Indian Journal of Pathology: Research and Practice 8, no. 4 (2019): 465–70. http://dx.doi.org/10.21088/ijprp.2278.148x.8419.14.

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30

Zewail, Ahmed. "Money counts but merit and freedom count even more." Nature 456, S1 (October 2008): 11. http://dx.doi.org/10.1038/twas08.11a.

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31

Bird, Sheila M. "Everyone counts—so count everyone in England and Wales." Lancet 387, no. 10013 (January 2016): 25–26. http://dx.doi.org/10.1016/s0140-6736(15)01291-x.

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32

Milgrom, Henry, Bruce Bender, and Frederick Wamboldt. "Assessing adherence with asthma medication: making the counts count." Annals of Allergy, Asthma & Immunology 88, no. 5 (May 2002): 429–31. http://dx.doi.org/10.1016/s1081-1206(10)62377-0.

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33

Faris, Richard, and Neil Paton. "121 Statistical Analysis Method Counts for Sow Count Data Responses." Journal of Animal Science 99, Supplement_1 (May 1, 2021): 56. http://dx.doi.org/10.1093/jas/skab054.094.

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Abstract Several statistical analysis methods are typically employed to analyze sow reproductive count data. The research objective was to compare analysis methods of pig birth counts to determine their robustness in identifying simulated treatment differences. Counts of stillborn (SB), born alive (BA) and sow parity differences were simulated using descriptive statistics from a sow farm. Different scenarios were tested: 1) Effect of a 0.5, 1.0, 1.5, and 2.0 percentage point change in treatment difference in SB and BA and, 2) Replicates of 20 to 200 experimental units (EU) in increments of 20 sows; yielding 40 total scenarios. For each scenario, sow observations were simulated 1000 times over. Random sub-setting was used to create a random effect of parity in each dataset as follows: 20% Parity 1, 50% Parity 2–4, and 30% Parity 5+ sows. Each simulated scenario was analyzed as: 1) General linear model (GLM) with raw counts of number of SB or BA as the response variable, 2) GLM with the ratio of BA or SB to total born as the response variable, and 3) Generalized linear mixed model (GLMM) with a binomial distribution of SB or BA as events and total born as trials. Across the EU replicate range, gross performance of models was compared by measuring area under the curve (AUC) with EU as abscissa and the probability of the simulation being P < 0.05 as ordinate. Simulation results are provided in Table 1. The GLMM has elevated probability of detecting true treatment differences over both GLM models for SB and BA. For BA analysis, the GLM Model 1 the probability of detecting true differences is greatly reduced vs. the other two models. This research indicates that deploying GLMM in analyses is a more effective and improved method to detect true differences in sow count data.

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34

Cooper, Richard, and Robert Eisner. "The Misunderstood Economy: What Counts and How to Count It." Foreign Affairs 73, no. 2 (1994): 145. http://dx.doi.org/10.2307/20045941.

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35

Gordon, David. "Medical schools: what counts as one, and why count them?" Medical Education 48, no. 9 (August 11, 2014): 844–45. http://dx.doi.org/10.1111/medu.12527.

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36

Krieger, N. ""Bodies Count," and Body Counts: Social Epidemiology and Embodying Inequality." Epidemiologic Reviews 26, no. 1 (July 1, 2004): 92–103. http://dx.doi.org/10.1093/epirev/mxh009.

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37

Glauber, James H., and Anne L. Fuhlbrigge. "Stratifying asthma populations by medication use: how you count counts." Annals of Allergy, Asthma & Immunology 88, no. 5 (May 2002): 451–56. http://dx.doi.org/10.1016/s1081-1206(10)62381-2.

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38

Modesti, Pietro A., and Ilaria Marzotti. "Epidemiology of hypertension and survey protocols: how to count counts." Hypertension Research 40, no. 5 (January 26, 2017): 432–33. http://dx.doi.org/10.1038/hr.2017.3.

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39

Cox, James L., Joseph F. Heyse, and John W. Tukey. "Efficacy Estimates from Parasite Count Data That Include Zero Counts." Experimental Parasitology 96, no. 1 (September 2000): 1–8. http://dx.doi.org/10.1006/expr.2000.4550.

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40

Kruijer, Willem, Alfred Stein, Willem Schaafsma, and Sanne Heijting. "Analyzing spatial count data, with an application to weed counts." Environmental and Ecological Statistics 14, no. 4 (September 6, 2007): 399–410. http://dx.doi.org/10.1007/s10651-007-0027-y.

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41

Holmes, John A., George M. W. Cronkite, Hermann J. Enzenhofer, and Timothy J. Mulligan. "Accuracy and precision of fish-count data from a “dual-frequency identification sonar” (DIDSON) imaging system." ICES Journal of Marine Science 63, no. 3 (January 1, 2006): 543–55. http://dx.doi.org/10.1016/j.icesjms.2005.08.015.

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Abstract The reliability of sockeye-salmon (Oncorhynchus nerka) count data collected by a dual-frequency, identification sonar (DIDSON) system is evaluated on the basis of comparisons with visual counts of unconstrained migrating salmon and visual counts of salmon constrained to passing through an enumeration fence. Regressions fitted to the DIDSON count data and the visual count data from the enumeration fence were statistically indistinguishable from a line with slope = 1.0 passing through the origin, which we interpret as agreement in both counts. In contrast, the regressions fitted to the DIDSON count data and the unconstrained visual count data had slopes that were significantly <1.0 (p < 0.001) and are consistent with an interpretation of systematic bias in these data. When counts of both unconstrained and constrained fish from the DIDSON system were ≥50 fish event−1, repeated counts of the DIDSON files were observed to produce the same counts 98–99% of the time, respectively, and based on the coefficient of variation, counts of individual passage events varied <3% on average. Therefore, the DIDSON count data exhibit high precision among different observers. As an enumeration fence provides a complete census of all fish passing through it, we conclude that fish-count data produced by the DIDSON imaging system are as accurate as visual counts of fish passing through an enumeration fence when counts range up to 932 fish event−1, the maximum count recorded during our study, regardless of the observer conducting the count. These conclusions should be applicable to typical riverine applications of the DIDSON system in which the bottom and surface boundaries are suitable for acoustic imaging, the migrating fish are adult salmon, and the transducer is carefully aimed so that the beams ensonify the area through which the salmon are migrating.

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42

McElwaine, Kathleen M., Megan Freund, Elizabeth M. Campbell, Carolyn Slattery, Paula M. Wye, Christophe Lecathelinais, Kate M. Bartlem, Karen E. Gillham, and John H. Wiggers. "Corrigendum to “Clinician assessment, advice and referral for multiple health risk behaviours: Prevalence and predictors of delivery by primary health care nurses and allied health professionals” [Patient Educ. Couns. 94 (2014) 193–201]." Patient Education and Counseling 99, no. 10 (October 2016): 1749. http://dx.doi.org/10.1016/j.pec.2016.06.026.

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43

Schildmann, Jan, and Jochen Vollmann. "Bombecke K et al., “Medical students trained in communication skills show a decline in patient-centred attitudes: An observational study comparing two cohorts during clinical clerkships” [Patient Educ. Couns. 11 (2011) 310–318]." Patient Education and Counseling 87, no. 3 (June 2012): 416. http://dx.doi.org/10.1016/j.pec.2011.11.004.

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44

Sunde, Peter, and Lonnie Jessen. "It counts who counts: an experimental evaluation of the importance of observer effects on spotlight count estimates." European Journal of Wildlife Research 59, no. 5 (April 7, 2013): 645–53. http://dx.doi.org/10.1007/s10344-013-0717-8.

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45

Desai, Amrita, and Adam J. Olszewski. "Evaluation of the physician-patient communication assessment tool (CAT) in a hematology-oncology practice." Journal of Clinical Oncology 31, no. 31_suppl (November 1, 2013): 14. http://dx.doi.org/10.1200/jco.2013.31.31_suppl.14.

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14 Background: Interpersonal communication skills are critical for building physician-patient relationships. The usefulness of feedback instruments to assess communication in oncology is uncertain. The simple, 15-item, patient-oriented CAT (Makoul G, et al. Patient Educ Couns 2007;67:333) was previously validated in non-oncologic specialties, using 20-30 samples per physician. The mean proportion of “Excellent” scores (MPES) was recommended as a summary estimate (76% in the above study). We evaluated the usefulness of this survey in a hematology-oncology practice. Methods: We administered CAT anonymously to all consenting outpatients during visits in our center, from January to March 2013. The association of “Excellent” scoring with patient age, gender and type of visit (cancer-related or unrelated) was studied using random-effects logistic regression. Results: We obtained 144 surveys (23-52 per physician). Median patient age was 65 years (range, 19-93) with 67% women and 54% cancer-related visits. The MPES for each survey question varied between 86% and 96%. The average MPES per physician was 92.1% (range 88.5-95.8%). MPES was higher for patients >65 years old (94.9% vs. 90.4%), men (96.7% vs. 90.3%), and for cancer-related visits (94.8% vs. 89.7%). In a multivariable model, the association was significant for age>65 (odds ratio, OR 5.38, 95%CI 1.06-27.5, p=.043) and female sex (OR 0.12, 95%CI 0.02-0.76, p=.024). The physician score strongly correlated with satisfaction with office staff recorded as part of the CAT (odds ratio, OR, 28.5, p<.0001). Conclusions: In a hematology-oncology practice, CAT results were considerably more skewed towards “Excellent” scores than in other previously studied specialties. Patients’ age and gender significantly influence the scores, which may confound comparisons between physicians or practices in quality assessment projects. As the needs, concerns and problems of cancer patients may differ from other medical settings, further research is needed to establish if CAT can be used for performance improvement, and whether oncology-specific questions can provide more discriminating measures of patient satisfaction and physician communication skills.

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46

Bhatt, Vijaya Raj, Prajwal Dhakal, Christopher S. Wichman, and Bunny J. Pozehl. "Therapy preference scale: A novel questionnaire to understand patients’ preferences of cancer treatment." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): e23179-e23179. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e23179.

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e23179 Background: Engaging patients in shared decision-making can help select an appropriate treatment for an individual and enhance patients’ experience and satisfaction with their care. This requires an in-depth understanding of patients’ values and preferences of various aspects of cancer treatment. A questionnaire to assess patient preferences is currently lacking. The purpose of our study was to develop a questionnaire that would fill this gap. Methods: We reviewed published literature to develop a theoretical framework to explain how cancer patients chose a specific therapy for themselves, and to identify key aspects of treatment that determine patients’ decisions about treatment. Three key domains (efficacy, safety and other characteristics of treatment such as need for hospital stay) were identified (Bhatt VR, J Geriatr Oncol. 2018 Nov 27 [Epub ahead of print]) and a mutli-dimensional self-report questionnaire with 49 items was developed. Each item was rated for relevance by 10 experts (oncologists, oncology nurses, advanced practice providers, social workers and pharmacists) to assess content validity (Lawshe formula) (Meas Eval Couns Dev, 47: 79–86). Ratings of ‘very relevant’ or ‘relevant’ were used to identify an essential question. Experts also suggested refinement of items. Three community members and 9 patients evaluated the instrument for face validity. Results: A content validity index for the questionnaire was 0.82. Face validity was confirmed. Based on input from the various stakeholders, the number of items, content, and format of the questionnaire was revised. The revised questionnaire includes a total of 36 items under four sections: safety (14 items), efficacy (4 items), treatment characteristics (8 items) and global items (10 items). Conclusions: We have developed a novel tool and established content and face validity. This self-report questionnaire can be used to understand patients’ preferences of cancer treatment. Further psychometric studies are planned to evaluate reliability and validity. Our ultimate goal is to use this tool to enhance communication between physicians and patients and facilitate shared decision-making.

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47

Geiger, Sonja M., and Klaus Oberauer. "Reasoning with conditionals: Does every counterexample count? It’s frequency that counts." Memory & Cognition 35, no. 8 (December 2007): 2060–74. http://dx.doi.org/10.3758/bf03192938.

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48

Méndez-Martinez, Jorge Luis. "What Counts as “a” Sound and How “to Count” a Sound." Synthesis philosophica 34, no. 1 (2019): 173–90. http://dx.doi.org/10.21464/sp34112.

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This paper addresses the problem of sound individuation (SI) and its connection to sound ontology (SO). It is argued that the problems of SI, such as aspatiality, extreme individuation, indexical perplexity and duration puzzles are due to SO's uncertainties. Besides, I describe the views in SO, including the wave view (WV), the property view (PV), and the event view (EV), as Cassey O’Callaghan defends it. According to O’Callaghan, EV offers clear standards to individuate sounds. However, this claim is countered by the consideration that any view could also defend the standards in SO, and thus, EV does not solve any of the problems mentioned above. As a way of showing the difficulties inherited by sound’s inner ontology, the problem of its linguistic representation is also addressed. The problem of SI can be developed within the frame of the philosophy of language and, specifically, regarding the discussion about mass vs count-sortal terms. Is the term sound a mass or a count-sortal? It is shown that, for reasons pertaining SO, the decision regarding the case of sound as a mass or count-sortal term remains open. SI is, thus, covered from the SO to the philosophy of language.

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Sakthivel, K. M., and C. S. Rajitha. "Model Selection for Count Data with Excess Number of Zero Counts." American Journal of Applied Mathematics and Statistics 7, no. 1 (January 18, 2019): 43–51. http://dx.doi.org/10.12691/ajams-7-1-7.

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Miller, R., W. Brown, and C. Tudor-Locke. "255 How to count non-step activity in daily step counts." Journal of Science and Medicine in Sport 8 (December 2005): 145. http://dx.doi.org/10.1016/s1440-2440(17)30751-x.

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