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OBJECTIVE: The present study investigated personal values underlying career-related preferences among young adults deliberating on their career choice. As a fundamental component of one's identity, personal values can offer valuable insights into how individuals prioritize their career-related preferences. METHOD: We employed two complementary approaches: variable-centered and person-centered, using a sample of 636 young adults (69.5% women; Mage = 21.9, SD = 2.12). RESULTS: Using a variable-centered approach, we identified 14 value-expressive career-related preferences, demonstrating that individuals are less willing to compromise on these preferences than on non value-expressive preferences. Using a person-centered approach, we applied latent profile analysis to identify four groups of young adults with distinct value profiles: (1) growth-focus (n = 212; 33.3%), (2) protection-focus (n = 206; 32.4%), (3) self-focus (n = 122; 19.2%), and (4) social-focus (n = 96; 15.1%). Importantly, the four profiles varied in 18 of 31 career-related preferences, revealing the composition of values underlying these career preferences. Fourteen of these preferences were identified as value-expressive in a variable-centered approach. CONCLUSION: The two complementary approaches employed in the present study introduce a fine-grained understanding of the value-expressiveness of career-related preferences.
RESUMO
Classifying clients into career indecision types can facilitate tailoring interventions to each client's specific needs. The present research examined a typology of career indecision on 50 data sets (N = 31,527) representing diverse populations from 16 countries on five continents. Latent profile analyses of participants' responses on the Career Decision-Making Difficulties Questionnaire (CDDQ) revealed seven replicable career indecision types across samples: unmotivated, unrealistic, generally uninformed, occupations-uninformed, conflicted-uninformed, externally conflicted, and internally conflicted. Age emerged as a negligible predictor of career indecision types, whereas gender predicted membership in the unmotivated type, with men twice more likely to be unmotivated than women. The seven types were similarly predictive of career decision status, decision certainty, and decision self-efficacy. These results largely support using the CDDQ to differentially diagnose career indecision types based on 10 causes of career indecision in different countries, life stages, and genders. Classifying individuals based on their patterns of career decision-making difficulties supports tailoring individual career counseling or group interventions to clients' needs. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Assuntos
Escolha da Profissão , Tomada de Decisões , Humanos , Feminino , Masculino , Ocupações , Identidade de Gênero , CogniçãoRESUMO
Diagnosing the causes of clients' career indecision is among the first steps in career counseling. The present study applied latent profile analysis to identify career indecision types using the 10 difficulty scale scores of the Career Decision-Making Difficulties Questionnaire (Gati et al., 1996). In two random U.S. samples (Ntotal = 8,918; age range = 14-50), five profiles of career indecision were identified and replicated: (1) unmotivated (6%), (2) generally indecisive (31%), (3) unrealistic (12%), (4) uninformed (39%), and (5) conflicted (12%). Age and gender negligibly predicted career indecision type, thereby supporting the stability of the five-profile typology. Nonetheless, the female gender was associated with a greater likelihood of being classified as indecisive as opposed to unmotivated (OR = 2.13). Furthermore, the five types differed in career decision status (η² = .28) and perceived career decisional distress (η² = .29). Uninformed and conflicted individuals reported multiple career decision-making difficulties and were still considering many career alternatives, whereas the three remaining types had one salient difficulty and had already identified a few (or even one) preferred alternative(s). Typological classification of individuals based on their salient causes of career indecision facilitates intervention planning as well as prescreening clients for individual counseling or group interventions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Assuntos
Escolha da Profissão , Tomada de Decisões , Adolescente , Adulto , Cognição , Feminino , Humanos , Pessoa de Meia-Idade , Ocupações , Orientação Vocacional , Adulto JovemRESUMO
Importance: Contemporary studies raise concerns regarding the implications of excessive screen time on the development of autism spectrum disorder (ASD). However, the existing literature consists of mixed and unquantified findings. Objective: To conduct a systematic review and meta-analyis of the association between screen time and ASD. Data Sources: A search was conducted in the PubMed, PsycNET, and ProQuest Dissertation & Theses Global databases for studies published up to May 1, 2023. Study Selection: The search was conducted independently by 2 authors. Included studies comprised empirical, peer-reviewed articles or dissertations published in English with statistics from which relevant effect sizes could be calculated. Discrepancies were resolved by consensus. Data Extraction and Synthesis: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guideline. Two authors independently coded all titles and abstracts, reviewed full-text articles against the inclusion and exclusion criteria, and resolved all discrepancies by consensus. Effect sizes were transformed into log odds ratios (ORs) and analyzed using a random-effects meta-analysis and mixed-effects meta-regression. Study quality was assessed using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach. Publication bias was tested via the Egger z test for funnel plot asymmetry. Data analysis was performed in June 2023. Main Outcomes and Measures: The 2 main variables of interest in this study were screen time and ASD. Screen time was defined as hours of screen use per day or per week, and ASD was defined as an ASD clinical diagnosis (yes or no) or ASD symptoms. The meta-regression considered screen type (ie, general use of screens, television, video games, computers, smartphones, and social media), age group (children vs adults or heterogenous age groups), and type of ASD measure (clinical diagnosis vs ASD symptoms). Results: Of the 4682 records identified, 46 studies with a total of 562â¯131 participants met the inclusion criteria. The studies were observational (5 were longitudinal and 41 were cross-sectional) and included 66 relevant effect sizes. The meta-analysis resulted in a positive summary effect size (log OR, 0.54 [95% CI, 0.34 to 0.74]). A trim-and-fill correction for a significant publication bias (Egger z = 2.15; P = .03) resulted in a substantially decreased and nonsignificant effect size (log OR, 0.22 [95% CI, -0.004 to 0.44]). The meta-regression results suggested that the positive summary effect size was only significant in studies targeting general screen use (ß [SE] = 0.73 [0.34]; t58 = 2.10; P = .03). This effect size was most dominant in studies of children (log OR, 0.98 [95% CI, 0.66 to 1.29]). Interestingly, a negative summary effect size was observed in studies investigating associations between social media and ASD (log OR, -1.24 [95% CI, -1.51 to -0.96]). Conclusions and Relevance: The findings of this systematic review and meta-analysis suggest that the proclaimed association between screen use and ASD is not sufficiently supported in the existing literature. Although excessive screen use may pose developmental risks, the mixed findings, the small effect sizes (especially when considering the observed publication bias), and the correlational nature of the available research require further scientific investigation. These findings also do not rule out the complementary hypothesis that children with ASD may prioritize screen activities to avoid social challenges.