ライフサイエンスコーパス: marital

1 tment; and partner-reported satisfaction and marital adjustment at 28 to 30 weeks and 1 year, predict

2 al Adjustment Questionnaire and to the Locke Marital Adjustment Test before radiotherapy, between wee

3 gnificant effect on sexual satisfaction, and marital adjustment was not significantly improved in par

4 f life, body image, depressive symptoms, and marital adjustment were assessed by use of validated que

5 r; overall sexual function and satisfaction; marital adjustment; and partner-reported satisfaction an

6 Potential confounders (age, sex, income, and marital and immigrant status) and mediators (substance a

7 r after adjustment for maternal age, height, marital and smoking status, and interpregnancy interval.

8 oad, body mass index, education, season, and marital and socioeconomic status, not breastfeeding was

9 s: 'sexual' OR 'sexuality' OR 'intimacy' OR 'marital' AND 'ALS' OR 'Amyotrophic Lateral Sclerosis' OR

10 can football, ice hockey players, boxers and marital art fighters).

11 A growing share of such non-marital births identify the father, which can create a l

12 (P < 0.001), anxiety disorders (P < 0.001), marital break-up (P = 0.015), and physician visits for m

13 es, were slightly less likely to result in a marital break-up (separation or divorce) and were associ

14 tional off-line venues, but the findings for marital break-up and marital satisfaction remained signi

15 isorders (ARR, 1.41; 95% CI, 1.24-1.60), and marital breakup (ARR, 1.18; 95% CI, 1.13-1.23) in the 2

16 eptibility; and genetic tests as a factor in marital choice.

17 r own and their wife's functioning and their marital communication.

18 IL-1beta) was lower at wound sites following marital conflicts than after social support interactions

19 increased treatment-seeking for depression, marital counseling, or other emotional or psychological

20 uring the second admission, they discussed a marital disagreement.

21 pidemiological literature has suggested that marital discord is a risk factor for morbidity and morta

22 Marital discord is costly to children, families, and com

23 C experience considerable mental illness and marital disruption.

24 Adverse sentimental relationships that cause marital dissolution may involve a genetic component comp

25 In children, marital distress, conflict, and disruption are associate

26 e timing, divorce and widow transitions, and marital durations on mortality.

27 t, which was especially important within the marital dyad.

28 at sex differences are related to a nexus of marital experiences and associated health risks.

29 tcomes) and body image, menopausal symptoms, marital functioning, psychological distress, and health-

30 ntercourse frequency, relationship intimacy, marital functioning, psychological distress, or health-r

31 h infection, and only at the STI clinic were marital, genital ulcer disease, and HIV-infection status

32 Variations in the marital histories of parents and children also contribut

33 fessional behavior by introducing usage of a marital infidelity website as a measure of personal cond

34 a range of environmental factors reflecting marital instability, as well as psychopathology and crim

35 hich is manifested in reproductive behavior, marital life, and parents' pastimes and investments in t

36 ia and Rwanda occurred within serodiscordant marital or cohabiting relationships, depending on the se

37 ted with exceptional survival, and lack of a marital partner was associated with mortality before age

38 e if sex and the interaction between sex and marital/partner status were independent prognostic varia

39 variate analyses restricted to men were age, marital/partner status, and income.

40 istically for other factors such as smoking, marital/partnership status, and self-rated health and we

41 ticular importance in South Asia, patrilocal marital practices were used to frame gender differences

42 In response to marital pressures, participant had to reconcile same-sex

43 an interactions, especially sustained, close marital relationships, influence the gut microbiota.

44 r to determine the dynamic behavior of their marital relationships.

45 ce) and were associated with slightly higher marital satisfaction among those respondents who remaine

46 when they met their husbands and then their marital satisfaction and HC use every 4 mo for up to thr

47 In study 1, wives reported their marital satisfaction every 6 mo for 4 y and then reporte

48 They then reported their marital satisfaction every 6 months for the next 4 years

49 s were less likely to experience declines in marital satisfaction over time.

50 s, but the findings for marital break-up and marital satisfaction remained significant after statisti

51 Decreased marital satisfaction, disturbances in family dynamics, a

52 of osteoporosis, hypothyroidism, employment, marital satisfaction, divorce, or psychological health.

53 fe, feeling sexually attractive, body image, marital satisfaction, quality of life, medical history,

54 r conscious ones, predicted changes in their marital satisfaction, such that spouses with more positi

55 h, neuroticism, divorce, social support, and marital satisfaction.

56 demonstrated no consistent associations with marital satisfaction.

57 efforts remain focused on abstinence and non-marital sex.

58 ssociated with HIV acquisition risk: gender, marital/sexual activity status, geographic location, "ke

59 sibs are expected to be sensitive to cues of marital stability, and these dispositions may be subject

60 Families expecting marital stability, unprepared for disruption, may experi

61 io 1.23, 95% CI 1.14-1.32; p<0.0001), single marital status (1.47, 1.18-1.83; p<0.0001), unemployment

62 k ratio [RR] 1.52, 95% CI 1.48-1.56), single marital status (1.70, 1.66-1.75), age 30-39 years (1.58,

63 unknown 35%, HIV-negative 8%; p < 0.001) and marital status (28% unmarried versus married 21%; p = 0.

64 e, race, ethnicity, geographic location, and marital status (adjusted hazard ratio, 2.19; 95% CI, 1.8

65 HPV among women in the control arm included marital status (adjusted odds ratio [AOR], 3.2; 95% conf

66 anisms that lead from specific dimensions of marital status (and family structure more broadly) to mo

67 [aOR] 0.5 [95% CI, .3-.9] >/=28 vs 22-23]); marital status (aOR 2.3 [95% CI, 1.5-3.5] single vs marr

68 higher risk for periodontitis, regardless of marital status (odds ratio: 1.5, 95% CI: 1.05 to 2.04, P

69 rder (OR = 1.35), widowed/separated/divorced marital status (OR = 1.28), and arthritis (OR = 1.27) we

70 of receiving chemotherapy included unmarried marital status (OR, 0.59; p = 0.0002) and more comorbidi

71 ikelihood of receiving RT included unmarried marital status (OR, 0.64; p < 0.0001) and more comorbidi

72 Social determinants of health, including marital status (SHR [95% CI]: 0.6 [0.4-0.9]), religious

73 ,000 dollars (OR = 5.6, 95% CI, 4.5 to 7.1), marital status (widowed/divorced/separated; OR = 1.3; 95

74 acteristics (age [P = .007], race [P = .05], marital status [P = .04]), personality (extroversion [P

75 d with race/ethnicity, education, income, or marital status after adjusting for age.

76 rthritis, pulmonary disease, stroke, asthma, marital status also contribute to MCI risk, which is les

77 Medication use, education, and marital status also influenced who underwent biopsy.

78 ntuitive that circumstantial factors such as marital status and age influence loneliness, there is al

79 f women of reproductive age defined by their marital status and contraceptive need and use, and the s

80 ationship, adjusted for age and sex, between marital status and dementia.

81 nt for family socioeconomic status, maternal marital status and education, children's nutrition, and

82 age at outcome assessment, prepregnancy BMI, marital status and insurance at delivery, race, smoking

83 ation ratio) and individual-level mediators (marital status and last dental visit).

84 l measures oversimplify the relation between marital status and mortality and that sex differences ar

85 ibutions to research on the relation between marital status and mortality.

86 iety and depression, IQ, educational status, marital status and pending litigation.

87 investigated one of the SNPs for measures of marital status and quality.

88 analyses, many nonclinical factors, such as marital status and region of the country, had a greater

89 ith infection were: maternal age, education, marital status and religion; household drinking water so

90 The association between marital status and risk for first registration for alcoh

91 dered personal and life circumstances (e.g., marital status and seniority) when discussing intention

92 Medical conditions as well as ethnic and marital status and smoking habits were considered.

93 We ascertained history of marital status and spouse's death by record linkage to t

94 alysis of studies of the association between marital status and the risk of developing dementia.

95 ing to personal characteristics such as age, marital status and urban or rural residence.

96 atus, tumor size, differentiation, race, and marital status are valuable for prognostication in breas

97 an differences in antisocial behavior across marital status at age 29 years were present even at 17 a

98 Marital status became the second most important factor a

99 Network score and marital status combined explained 27% (95% confidence in

100 evated child problem behaviors, and divorced marital status conveyed elevated risk for psychiatric di

101 requently cited "red flag." Age, gender, and marital status did not affect how applicants rated facto

102 igh-income versus low-income, and racial and marital status disparities.

103 3) Marital status has a limited impact on periodontal healt

104 tion between periodontitis risk, gender, and marital status in older adults.

105 ctivity, alcohol consumption, education, and marital status in pooled data from 19 prospective studie

106 lyze the survival difference among different marital status in the United States.

107 er tumor size (OR, 0.37; 95% CI, 0.27-0.51), marital status of being separated at the time of diagnos

108 ers, 50% were single and 20.6% were married (marital status of the remainder was unknown).

109 The influence of marital status on racial/ethnic disparities was stronger

110 For CD and ND in the EA population, marital status proved to be a significant moderator in t

111 fter adjusting for changes in disability and marital status since baseline (OR, 1.72; 95% CI, 0.99-2.

112 ces of neighborhood socioeconomic status and marital status suggest that social determinants, support

113 atements, white race, older age, and married marital status to be associated with higher adherence (a

114 e than a century of empirical evidence links marital status to mortality.

115 Marital status was found to be an independent prognostic

116 ariable regression analysis showed that only marital status was independently associated with higher

117 blings, and monozygotic twins discordant for marital status were as strong as that seen in the genera

118 Parent education and marital status were significantly associated with BA and

119 causes, but education, occupation, sex, and marital status were typically included as well.

120 e, sex, race/ethnicity, education level, and marital status) and health factors (OA severity, knee sy

121 ctors (age, education, household income, and marital status) were included in the analysis.

122 ), sociodemographic characteristics (age and marital status), and treatment (surgery and radiation th

123 ce with adjustment for individual (age, sex, marital status), clinical (histologic grade, surgery, ir

124 demographics (age, sex, racial/ethnic group, marital status), comorbid conditions, and economic resou

125 istics (i.e., age, sex, race, education, and marital status), employment status at year of diagnosis,

126 tal health status, maternal IQ, and maternal marital status).

127 y quintiles, highest level of education, and marital status).

128 , diabetes mellitus, years of education, and marital status).

129 ustment for age, sex, education, income, and marital status, acculturation was negatively associated

130 Results were similar after adjustment for marital status, age, and education.

131 fects meta-analysis, controlling for region, marital status, age, number of sex partners, and condom

132 to maternal variables (age, race, education, marital status, alcohol during pregnancy) or child varia

133 ation functional class, depression symptoms, marital status, and baseline beta-blockers, angiotensin-

134 Age, marital status, and comorbidities influenced the probabi

135 re adjusted for age, sex, educational level, marital status, and CVD risk factors.

136 ter further adjustment for health behaviors, marital status, and education.

137 sted for height, weight, smoking, education, marital status, and energy intake.

138 erval: 0.09 to 0.72), adjusting for smoking, marital status, and gender.

139 tation correlated with sex, race, ethnicity, marital status, and geographic region (ethnicity, P = .0

140 lower mammography rates based on age, race, marital status, and geographic region.

141 r the correlated variables of family income, marital status, and health insurance type.

142 , adjusting for age, sex, educational level, marital status, and healthcare worker status.

143 ng for child's age and sex, mother's age and marital status, and household size.

144 r adjusting for insurance, hospitalizations, marital status, and illness severity.

145 ent risk factors for HPV detection were age, marital status, and increasing numbers of lifetime and r

146 ic variables including sex, race, ethnicity, marital status, and insurance status are associated with

147 al surgery, higher disease stage, older age, marital status, and lower comorbidity.

148 ting for maternal education, race/ethnicity, marital status, and maternal age; separately examining h

149 evel data on diagnosis, age, sex, ethnicity, marital status, and occupational status, and our outcome

150 tment for matching characteristics, smoking, marital status, and race/ethnicity using logistic regres

151 Asian, or Indigenous, and educational level, marital status, and region of residence) across the stud

152 eg, sex, age, education, income, urbanicity, marital status, and regional differences) and mental, ne

153 ifferences in employment, social assistance, marital status, and reproduction were no longer signific

154 Baseline variables of age, sex, marital status, and risk did not modify vaccine efficacy

155 Age group, education, marital status, and sexual behavior were associated with

156 bling group, own socioeconomic position, own marital status, and socioeconomic rank within the siblin

157 information in our analyses, such as gender, marital status, and the presence of current or previous

158 In men, marital status, and to a lesser extent network score (bu

159 The predictive value of age, race, marital status, and tumor characteristics were compared.

160 emale sex in Alaska only, whereas education, marital status, and urban residency were associated with

161 ite race, Hispanic ethnicity, education, and marital status, as well as zip code population character

162 lus, crown coverage, age, income, education, marital status, body mass index, diabetes, and vitamin C

163 king and variables to control for education, marital status, body mass, alcohol consumption, occupati

164 functional independence measure score), age, marital status, chronic conditions, and prestroke ambula

165 ompletion of treatment, including age, race, marital status, comorbidities, and sociodemographic fact

166 moking, social class, long-standing illness, marital status, diabetes, hypertension)-adjusted hazard

167 d with the use of radiation therapy included marital status, disease stage, and type of lymph node su

168 characteristics (age, sex, race, education, marital status, distance to nearest acute care hospital)

169 ion, 197 control), similar across groups for marital status, duration of HIV diagnosis, and distance

170 sociation between self-assessment scores and marital status, education level, performance status, or

171 on and adjusted for maternal race/ethnicity, marital status, education, age, and ozone.

172 ssion, adjusted for maternal race/ethnicity, marital status, education, age, smoking, maximum tempera

173 ing variables of maternal age, parity, race, marital status, education, family income, smoking, alcoh

174 model: study site, maternal age, gravidity, marital status, education, race/ethnicity, smoking, and

175 ty, baseline weight (and their interaction), marital status, education, smoking, calorie intake, and

176 r controlling for age, time since diagnosis, marital status, education, tumor site and stage, comorbi

177 fter adjusting for age, sex, race/ethnicity, marital status, educational attainment, employment, heal

178 ing individual characteristics (gender, age, marital status, educational background and self-rated he

179 were categorized by patient characteristics (marital status, educational level) and tumor characteris

180 er of people in household, country of birth, marital status, educational level, and highest employmen

181 nces in kappa statistics based on age, race, marital status, educational level, and income.

182 fter adjustment for comorbidity score, race, marital status, educational status, clinical stage, and

183 cioeconomic markers (education, age, income, marital status, employment status) on these conditions.

184 f age, sex, social class, educational level, marital status, employment status, body mass index, phys

185 patient age, race, education, health status, marital status, employment status, distance from the cen

186 h state and trait anxiety were: age, gender, marital status, employment status, level of education, s

187 ogistic regression, adjusting for sex, race, marital status, employment, education, health literacy,

188 ctive women aged 15-49 years irrespective of marital status, except in Argentina and Brazil, where an

189 -ethnicity, breastfeeding, mode of delivery, marital status, exposure to environmental tobacco smoke,

190 idence after controlling for age, education, marital status, fasting glucose, body mass index, high-d

191 elf-explanatory outcomes such as employment, marital status, financial independence and housing.

192 re consistent with finer-grained analyses by marital status, gender, and age.

193 ds models, adjusted for age, sex, ethnicity, marital status, geographical region, childhood and adult

194 aditional measures such as socioeconomic and marital status, health habits, and education may require

195 Age, marital status, health status, stressful life events, an

196 Assessment of marital status, heart failure symptomatology, and percei

197 rs of better QOL included college education, marital status, higher household income, private health

198 y performance status, stage, sex, age, race, marital status, histology, tumor location, hemoglobin, t

199 ust, controlling for age, gender, education, marital status, household income and religiosity.

200 Age, gender, marital status, household structure, and occupation did

201 Hazard ratios (HRs) were adjusted for marital status, immigration status, income quartile (sin

202 lic and diastolic blood pressure, education, marital status, income, and occupation.

203 ratios (aOR), by adjusting for nationality, marital status, income, and site of testing.

204 factors including age, ethnicity, education, marital status, income, employment, and drug and alcohol

205 Correlates of depressive symptoms include marital status, income, kidney function, history of affe

206 er adjusting for age, background, education, marital status, income, nativity, smoking, physical acti

207 respect to age, ethnicity, race, education, marital status, income, obstetric history, and language.

208 groups (sex, age, race/ethnicity, education, marital status, income, urban/rural, and region).

209 ancy BMI, previous history of preterm birth, marital status, infant sex, and initiation of prenatal c

210 regnancy, previous history of preterm birth, marital status, infant sex, and timing of initiation of

211 conomic variables including race, ethnicity, marital status, insurance status, and geographic locatio

212 6) after adjusting for age, smoking, parity, marital status, insurance status, and weight.

213 ex), adjusting for survey year, region, age, marital status, insurance, educational attainment, and i

214 ing status and intensity, educational level, marital status, job status, energy intake, and physical

215 djusted for age, baseline educational level, marital status, leisure time physical exercise, walking/

216 physical health status, depressive symptoms, marital status, level of education, and severity of illn

217 er controlling for age, sex, race/ethnicity, marital status, living alone or not, education, income,

218 health, education level, employment status, marital status, living arrangements, and birth rate were

219 ssociated with lower age, male sex, divorced marital status, living with children, lack of satisfacti

220 onomic status, lived in urban areas, married marital status, lower PSA levels and lower Gleason score

221 ents, age at presentation, older age, single marital status, lower socioeconomic status, higher PSA l

222 adults to investigate the impact of current marital status, marriage timing, divorce and widow trans

223 orrelates included in our analyses were age, marital status, marriage type, whether pregnant or post

224 After adjusting for age, marital status, material deprivation history, smoking, d

225 ages over childhood irrespective of parental marital status, may anticipate or otherwise accommodate

226 Information regarding income, education, marital status, medical history, and cardiovascular risk

227 alyses revealed that age more than 65 years, marital status, minority populations, and primary tumor

228 y, child's ethnicity, mother's age, mother's marital status, mother's depression score at 18 and 32 w

229 usting for country, maternal age, education, marital status, neonate weight at birth, and neonate sex

230 ties differed from whites in sex proportion, marital status, number of children, geographic location,

231 e no significant differences with respect to marital status, number of children, or number of hours w

232 and MCS after adjustment for age, education, marital status, number of comorbidities, smoking, cancer

233 Fisher exact test showed that gender, age, marital status, occupation and level of education had no

234 ollowing characteristics: gender, age, race, marital status, parental status, additional graduate deg

235 or smoking, body mass index, alcohol intake, marital status, parity, menopausal status, and history o

236 After adjustment for race/ethnicity, marital status, parity, prepregnancy physical activity,

237 f melanoma, institution, histologic subtype, marital status, performance of skin self-examination, nu

238 ing status, alcohol intake, body mass index, marital status, physical activity, and education level.

239 ation level, sex, US region, race/ethnicity, marital status, political affiliation, likelihood to vot

240 logistic regression with adjustment for age, marital status, prescription drug monitoring programs, a

241 Data for age, sex, ethnicity, marital status, psychotic disorder diagnosis, subsequent

242 sible modifiers of concordance: patient age, marital status, race, educational level, Eastern Coopera

243 After adjustment for maternal age, marital status, race/ethnicity, and education, the risk

244 After controlling for age, gender, marital status, race/ethnicity, city, and time, the auth

245 ction, race/ethnicity, socioeconomic status, marital status, referral source, and referral year.

246 ent for a participant's age, sex, education, marital status, religion, and study site.

247 regression that adjusted for age, race, sex, marital status, residence, percent of county below feder

248 When adjusted for age, marital status, residential area, clinical characteristi

249 After adjusting for age, education, race, marital status, self-efficacy, and dental knowledge, mul

250 conomic variables, including age, race, sex, marital status, service connection, prescription copay,

251 ge, surgery weight, surgery body mass index, marital status, sex, educational level, site, Internatio

252 for country, age, race, education duration, marital status, smoking, alcohol, and number of recent s

253 ed to age, state, race/ethnicity, education, marital status, smoking, and alcohol consumption.

254 after adjusting for age, gender, education, marital status, smoking, hypertension, diabetes mellitus

255 Sex, age, ethnicity, marital status, social deprivation, severity of psychopa

256 Three components (marital status, social network size, and religious servi

257 s measured with a 7-item index that included marital status, social network size, frequency of contac

258 s measured with a 7-item index that included marital status, social network size, frequency of contac

259 ciations were not accounted for by sex, age, marital status, socioeconomic position, place of work, s

260 ciocultural factors, such as age, gender and marital status, strongly influence the probability of un

261 e includes 5 domains (employment, education, marital status, substance abuse and income), each with a

262 al radiation, age, sex, race, education, and marital status, survivor hair loss increased risk of anx

263 fter adjustment for age, race/ethnicity, and marital status, the odds of high-risk HPV infection were

264 phic factors at study inclusion such as age, marital status, the presence of children in the home, ar

265 adults to examine the association of current marital status, timing of first marriage, number and kin

266 ables included individual factors (age, sex, marital status, underlying cause of death) and measures

267 Higher LS was significantly related to marital status, unilateral affliction, and higher educat

268 xyvitamin D levels and age, body mass index, marital status, use of hormone therapy, physical activit

269 interaction; and the PFS model also included marital status, weight loss, and p16 x Zubrod interactio

270 ditionally, patients with single or divorced marital status, who were living in rural places had high

271 adjusted for hormonal contraceptive use and marital status, women reporting multiple male partners o

272 rlson score, residency training program, and marital status, women with early-stage disease were sign

273 ber and specific causes), age at death, sex, marital status, year of death, index of multiple depriva

274 untry of birth, language spoken at home, and marital status.

275 = 0.29), but did not differ by age, sex, or marital status.

276 tion, knowledge about malaria/ITNs, age, and marital status.

277 breastfeeding, age, education, smoking, and marital status.

278 economic status, rural residence, and single marital status.

279 n, household wealth quintile, education, and marital status.

280 nformation on educational qualifications and marital status.

281 .9) after adjustment for age, education, and marital status.

282 , poor sleep, health status, employment, and marital status.

283 y, parity, height, body mass index, race, or marital status.

284 conditions were found between spouses or by marital status.

285 ptor (PR) status, differentiation, race, and marital status.

286 ies according to age, race or ethnicity, and marital status.

287 n, health behaviours, employment status, and marital status.

288 cts could be explained by differences in: 1) marital status; 2) gender; and 3) ethnicity.

289 mic status; residence area; occupation type; marital status; history of hypertension, diabetes mellit

290 ecific antigen (PSA) levels, Gleason scores, marital statuses and bone metastasis statuses was compar

291 ent, how many children parents have, and the marital statuses of parents and children affect the exte

292 of marriage, and durations spent in various marital statuses with the risk of all-cause mortality.

293 f particular types of stress such as job and marital strain, with recurrent adverse events after AMI.

294 considered: the parent-child subsystem, the marital subsystem, and the sibling subsystem.

295 stics, our best scholars have concluded that marital therapy is at a practical and theoretical impass

296 e hazards of dying associated with long-term marital trajectories and contributing risk factors are l

297 An analysis of the raiding histories, marital trajectories, and reproductive histories of thes

298 essors are discussed in terms of job stress, marital unhappiness, and burden of caregiving.

299 itions - be used to facilitate relational or marital well being?

300 are hormonal contraceptives (HCs) related to marital well-being?