Adolescent Alcohol Use in Spain: Connections with Friends, School, and Other Delinquent Behaviors

This study examined the connections between adolescent alcohol use in Alicante, Spain and variables reflecting adolescents’ academic problems, potentially delinquent behaviors, friends’ alcohol consumption, and friendship quality. Information about alcohol use and a number of school and social variables was collected from adolescent students (N = 567) who completed the National Students School-Based Drug Survey in a classroom setting. 

Results suggested that gender was not significantly associated with alcohol use, although alcohol use increased with age and was more likely for adolescents enrolled in public schools compared to private. After controlling for age and type of school (public vs. private), academic problems explained 5.1% of the variance in adolescents’ alcohol use, potentially delinquent behaviors explained 29.0%, friends’ alcohol use 16.8%, and friendship quality 1.6%. 

When all unique predictors from these four models were included in a comprehensive model, they explained 32.3% of the variance in adolescents’ alcohol use. In this final model, getting expelled, participating in a fight, going out at night, the hour at which one returns, and the number of friends who have consumed alcohol were uniquely and positively associated with adolescents’ alcohol use. 

These results provide important information about multi-system influences on adolescent alcohol use in Alicante, Spain and suggest potential areas of focus for intervention research.

Correlations between alcohol consumption and all predictors.

Predictors	Correlation (r) with alcohol consumption
Demographics
Age	0.25∗∗
Public vs. private school	0.10∗
Gender	0.03
Academic Problems
Normally I do not complete any of my homework.	0.04
Have you repeated a course?	0.30∗∗
Have you missed class in the past 30 days?	0.13∗∗
Have you been expelled in the past 12 months?	0.19∗∗
Potentially delinquent behaviors
Have you run away from home in the past 12 months?	0.14∗∗
Have you participated in a fight in the past 12 months?	0.26∗∗
Do you go out at night?	0.52∗∗
When do you return when you go out at night?	0.41∗∗
Friends’ alcohol consumption
How many of your friends consumed alcohol in the past 30 days?	0.42∗∗
How many of your friends got drunk in the past 30 days?	0.37∗∗
Friendship quality
I receive care from my best friend.	0.03
How often do you hang out with your friends?	0.11∗
Friends subscale of the KIDSCREEN-52	0.09∗

∗∗ Correlation is significant at the 0.01 level (two-tailed). ∗ Correlation is significant at the 0.05 level (two-tailed).

Full article at:   http://goo.gl/P5Isu7

By:  Lisa D. Goldberg-Looney,1 Miriam Sánchez-SanSegundo,2 Rosario Ferrer-Cascales,2 Natalia Albaladejo-Blazquez,2 andPaul B. Perrin1,*

¹Department of Psychology, Virginia Commonwealth University, Richmond, Virginia, USA

²Department of Health Psychology, University of Alicante, Alicante, Spain

Edited by: Gianluca Castelnuovo, Università Cattolica del Sacro Cuore, Italy

Reviewed by: Michelle Dow Keawphalouk, Harvard and Massachusetts Institute of Technology, USA; Roberto Cattivelli, Istituto di Ricovero e Cura a Carattere Scientifico, Italy

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Sexual Behaviour & Risk of Sexually Transmitted Infections in Young Female Healthcare Students in Spain

Background. 

Several authors have examined the risk for sexually transmitted infections (STI), but no study has yet analyzed it solely in relation with sexual behaviour in women. We analyzed the association of sexual behaviour with STI risk in female university students of healthcare sciences.

Methods. 

We designed a cross-sectional study assessing over three months vaginal intercourse with a man. The study involved 175 female university students, without a stable partner, studying healthcare sciences in Spain. Main outcome variable: STI risk (not always using male condoms). Secondary variables: sexual behaviour, method of orgasm, desire to increase the frequency of sexual relations, desire to have more variety in sexual relations, frequency of sexual intercourse with the partner, and age. The information was collected with an original questionnaire. A logistic regression model was used to estimate the adjusted odds ratios (ORs) in order to analyze the association between the STI risk and the study variables.

Results. 

Of the 175 women, 52 were positive for STI risk (29.7%, 95% CI [22.9–36.5%]). Factors significantly associated with STI risk (p < 0.05) included: orgasm (not having orgasms →OR = 7.01, 95% CI [1.49–33.00]; several methods →OR = 0.77, 95% CI [0.31–1.90]; one single method →OR = 1; p = 0.008) and desiring an increased frequency of sexual activities (OR = 0.27, 95% CI [0.13–0.59], p < 0.001).

Conclusions. 

Women’s desire for sexual activities and their sexual function were significant predictors of their risk for STI. Information about sexual function is an intrinsic aspect of sexual behaviour and should be taken into consideration when seeking approaches to reduce risks for STI.

Analysis of STI risk in female university students from Alicante (Spain). 2005–2009 data.

Variable	Total	STI risk	Adj. OR	95% CI	p-value
	175	52(29.7%)
	n(%)∕x ± s	n(%)∕x ± s
Sexual orientation:
Heterosexual	168(96.0)	48(28.6)	N/M	N/M	N/M
Bisexual or other*	7(4.0)	4(57.1)
Method of orgasm:
No orgasm	13(7.4)	9(69.2)	7.01	1.49–33.00	0.008a
Several methods	131(74.9)	32(24.4)	0.77	0.31–1.90
A single method*	31(17.7)	11(35.5)	1
Desire to increase the frequency of sexual relations:
Yes	92(52.6)	18(19.6)	0.27	0.13–0.59	<0.001
No*	83(47.4)	34(41.0)	1
Desire to have more variety in sexual relations:
Yes	60(34.3)	13(21.7)	0.58	0.27–1.26	0.168
No*	115(65.7)	39(33.9)	1
Age (years)	20.8 ± 2.2	20.9 ± 2.0	N/M	N/M	N/M
Frequency of sexual intercourse with the partner	3.8 ± 1.2	4.0 ± 1.4	1.17	0.86–1.60	0.396

Notes.

STI

Sexually transmitted infections

Adj. OR

adjusted odds ratio

CI

Confidence interval

N/M

Not in the model

*Reference.

ap-value for the complete factor. The p-values for the comparison with the reference are: (1) No orgasm: 0.014; (2) Several methods: 0.571. Frequency of sexual intercourse with partner (6 = 5–7 times/week; 5 = 3–4 times/week; 4 = 1–2 times/week; 3 = 2–3 times/month; 2 = once/month; 1 = Never). Goodnessof-fit of the model: (1) likelihood ratio test: X2 = 26.1, p < 0.001; (2) Hosmer-Lemeshow test: X2 = 13.6, p = 0.092.

Below:  Predicted probabilities of STI risk in relation to Methods of Orgasm category in female university students from Alicante (Spain). 2005–2009 data

Below:  Predicted probabilities of STI risk in relation to Desire to increase the frequency of sexual relationships in female university students from Alicante (Spain). 2005–2009 data

Purchase full article at:  http://goo.gl/Cr86qZ

By:  Felipe Navarro-Cremades,1 Antonio Palazón-Bru,1 Dolores Marhuenda-Amorós,2 María Isabel Tomás-Rodríguez,2 Fina Antón-Ruiz,3 Josefina Belda-Ibañez,4 Ángel Luis Montejo,5 and Vicente Francisco Gil-Guillén1

¹Department of Clinical Medicine, Miguel Hernández University, San Juan de Alicante, Alicante, Spain

²Department of Pathology and Surgery, Miguel Hernández University, San Juan de Alicante, Alicante, Spain

³Department of Education, San Antonio Catholic University, Murcia, Spain

⁴Centre for Information and AIDS Prevention, Conselleria de Sanitat, Alicante, Spain

⁵Department of Nursing, University of Salamanca, Salamanca, Spain

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Predicting Condom Use in Adolescents: A Test of Three Socio-Cognitive Models Using a Structural Equation Modeling Approach

Background

The theory of planned behavior (TPB), socio-cognitive model (SCM), and information-motivation-behavioral skills (IMB) model are effective in predicting condom use. However, the adequacy of these three theoretical models in predicting the frequency of condom use (FCU) among young people has not been compared. This cross-sectional study tested the applicability and suitability of these three models in predicting the FCU, and analyzed the relationships among the postulated constructs.

Methods

Sexually experienced adolescents (n = 410) aged 13–18 completed a survey assessing the TPB, SCM, and IMB model constructs. Participants were students recruited from 18 high schools, randomly selected from the north, south, east, and southeast of Spain. A structural equation modelling (SEM) analysis was applied to test TPB, SCM and IBM and constructs relationships of each model using R.

Results

The results of SEM demonstrated that behavioral skills predict behavior via motivation as hypothesized by the IMB model, but not directly via knowledge about condom use and sexually transmitted infections (STIs). Cognitive factors, such knowledge about condom use and STIs as well as condom use self-efficacy, directly predicted the FCU when modeled as per the SCM. According to the TPB, condom use intention was the best predictor of the FCU, and condom use intention was predicted by attitudes toward condom use and subjective norms related to condom use, but perceived control was not directly or indirectly related to the FCU. Based on the data, the TPB becomes the best-fit model for predicting the FCU among young people compared to the SCM and IMB model.

Conclusions

From a statistical perspective, the TPB seems to be the most suitable model for predicting the FCU among young people compared to the other models. Overall, key direct predictors of the FCU in adolescents included condom use intention, behavioral skills and cognitive factors, such as STIs knowledge and condom use self-efficacy. The next step should be to test integrative models that include personal, contextual, environmental, and social factors.

Full article at:   http://goo.gl/iT5Wqw

By:  Espada JP1, Morales A2, Guillén-Riquelme A3, Ballester R4, Orgilés M5.

• 1Department of Health Psychology, Miguel Hernández University, Av. de la Universidad, s/n, 03202, Elche, Alicante, Spain. jpespada@umh.es.
• 2Department of Health Psychology, Miguel Hernández University, Av. de la Universidad, s/n, 03202, Elche, Alicante, Spain. alexandra.moraless@umh.es.
• 3Department of Personality, Evaluation and Psychological Treatment, University of Granada, Campus Universitario de Cartuja, 18011, Granada, Spain. aguillenriquelme@ugr.es.
• 4Department of Basic and Clinical Psychology and Psychobiology, Jaume I University of Castellón, Avda. Sos Baynat, s/n, 12071 Castellón de la Plana, Castellón, Spain. rballest@psb.uji.es.
• 5Department of Health Psychology, Miguel Hernández University, Av. de la Universidad, s/n, 03202, Elche, Alicante, Spain. morgiles@umh.es. 

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