ORIGINAL RESEARCH published: 20 July 2021 doi: 10.3389/fpsyg.2021.694868 Career Planning in Elite Soccer: The Mediating Role of Self-Efficacy, Career Goals, and Athletic Identity Ricardo Monteiro 1 , Diogo Monteiro 2,3 , Miquel Torregrossa 4 and Bruno Travassos 1,3,5* 1 Department of Sport Sciences, Universidade da Beira Interior, Covilhã, Portugal, 2 ESECS, Polytechnic of Leiria, Leiria, Portugal, 3 CIDESD, Research Center in Sports Sciences, Health Sciences and Human Development, Covilhã, Portugal, 4 Departament de Psicologia Clínica i de la Salut, Universitat Autònoma de Barcelona, Barcelona, Spain, 5 Portugal Football School, Portuguese Football Federation, Oeiras, Portugal This study proposed a model to examine the role of self-efficacy, career goals, and athletic identity (AI) on the career planning of elite soccer players. Two hundred and eighty-one elite soccer players (males) participated in this study. Means, SD, and bivariate correlations were calculated for the variables under analysis. The hypothesized effect of self-efficacy, career goals, and AI on career planning was tested using structural Edited by: Antonio Hernández-Mendo, equation modeling (SEM). Results supported the adequacy of the proposed model in University of Malaga, Spain explaining the career planning. Career planning is positively influenced by the level of Reviewed by: self-efficacy of players through the definition of career goals and is negatively influenced Cristina Lopez De Subijana, Polytechnic University of Madrid, by the level of AI. At the same time, the self-efficacy of players through the definition of Spain career goals positively influenced AI. These findings reinforce the important role of self- Michael John Stones, efficacy and career goals for the development of AI and career planning and at the same Lakehead University, Canada Claudio Imperatori, time the opposite relationship between AI and career planning. Thus, it is suggested that European University of Rome, Italy a balance on AI, maintaining high levels of self-efficacy and career goals, is required to *Correspondence: improve the process of career planning and retirement. Bruno Travassos bfrt@ubi.pt Keywords: career planning for life after sport, social cognitive factors, soccer players, career management, athletic identity, self-efficacy Specialty section: This article was submitted to Movement Science and Sport INTRODUCTION Psychology, a section of the journal Understanding the career of players through the umbrella of the Holistic Athletic Career Model Frontiers in Psychology (Wylleman, 2019) underlines a holistic life span and “beginning-to-end” approach of the career Received: 13 April 2021 development of athletes, describing career pathways and predicting normative transitions between Accepted: 17 May 2021 adjacent stages in a multilayer process. The predictability of normative transitions, such as career Published: 20 July 2021 retirement, creates an opportunity to prepare athletes to cope with them in advance (Stambulova Citation: et al., 2009). In fact, the career of players is a process that should be prospectively managed to Monteiro R, Monteiro D, balance individual resources and barriers to prepare a smooth retirement and the life after sport Torregrossa M and Travassos B (Alfermann and Stambulova, 2007; Demulier et al., 2013). In line with the social cognitive career (2021) Career Planning in Elite theory (Lent and Brown, 2013), a positive relationship among self-efficacy, career goals, and career Soccer: The Mediating Role of Self-Efficacy, Career Goals, planning seems to be key individual resources to such processes (Demulier et al., 2013). and Athletic Identity. For instance, the self-efficacy of athletes has been one of the most important internal factors Front. Psychol. 12:694868. reported to explain successful career transitions (Wendling and Sagas, 2020). Self-efficacy could doi: 10.3389/fpsyg.2021.694868 be defined as the belief or ability of an individual to perform a specific task or behavior to bring Frontiers in Psychology | www.frontiersin.org 1 July 2021 | Volume 12 | Article 694868 Monteiro et al. Career Planning in Soccer forth the desired outcome (Bandura, 2004). According to Lent in relation to future career development related to the sport and Brown (2013), self-efficacy in career transitions refers to the (Cabrita et al., 2014). ability to manage specific tasks necessary with the preparation of Despite the previous study in the context of sport revealed such transitions, adjustments, or changes, i.e., the tasks related that career planning is influenced by the levels of self-efficacy, to career planning and management. In its turn, influenced by AI, career goals, consciousness, competence, or even some self-efficacy levels, career goals are defined as the intentions personality traits (Demulier et al., 2013; Cabrita et al., 2014; of athletes to be engaged with a certain task to achieve a Wendling and Sagas, 2020), to the best of our knowledge, any specific outcome. Thus, career goals influenced by self-efficacy study analyzed the role of self-efficacy, career goals, and AI on levels contribute to a better definition of career objectives career planning. This study proposed a model to examine the and the consequent steps or planning to achieve them. More role of self-efficacy, career goals, and AI on the career planning of precisely, career goals refer to the definition of the intended professional soccer players (see Figure 1). It is hypothesized that set of professional, life, and after sport life achievements, the (a) self-efficacy positively predicted career goals, career planning, subsequent plans to achieve them, and to face the expected and AI, (b) career goals positively predicted career planning and problems during the process (Creed et al., 2013; Demulier et al., AI, and (c) due to narrow focus in sports practice and despite 2013). When objectives are clearly identified, career plans are the positive relationship with self-efficacy and career goals, it was made to pursue those previously identified objectives, triggering expected that AI negatively predicted career planning. career achievements and facilitating transitions. Career planning is defined as the definition of the actions or behaviors needed for career development. This kind of task, specific to career MATERIALS AND METHODS management, seems to be a crucial aspect in the context of sports career termination (Rogers and Creed, 2011). Individuals Participants with high self-efficacy tend to better define career goals and A total of 281 active professional Portuguese soccer players (all plan their careers (Lent and Brown, 2013). In this regard, it is males) aged between 18 and 39 years (M = 26.23; SD = 4.96), reasonable to assume that definitions of self-efficacy and goals are with a mean of 18.85 ± 2.32 years of practice, from several first proximal antecedents of career planning (Demulier et al., 2013). and second professional league clubs in Portugal were recruited Besides the theoretical point of view among the variables, several to participate in this study. empirical studies have been shown the associations among self- efficacy, career goals, and career planning (e.g., Wendling and Sagas, 2020), which reinforces the importance of these variables Instruments in career transitions. The instruments (i.e., Self-Efficacy Scale-Short Form, Career Previous study also pointed out that individual differences Goal Setting Scale, and Career Planning Scale) previously not and the level of consciousness (Rogers et al., 2008) about career validated in Portuguese were translated to Portuguese and back- transitions and particularly the retirement have implications translated to the original scale (Beaton et al., 2000). In addition, to the level of career planning and consequently to the the factor structure of each scale was examined before testing the retirement. In line with that, the level of athletic identity (AI) hypothesized model. is another important internal factor that could contribute to explain career planning through variations in the consciousness Self-Efficacy about career development and retirement (Martin et al., 2014). The Self-Efficacy Scale-Short Form (Betz et al., 1996) was used High levels of AI may lead to an over-involvement and to measure the self-efficacy of athletes. This scale comprises commitment in sports practice, with a reduction of attention 12 items grouped into 1 factor (e.g., “select career options to other social and career aspects, consequently decreasing that best match your interests, values, skills, personality, and the consciousness about career management and development lifestyle”), which are preceded by the following sentence: “How (Cecić-Erpič et al., 2004). Variations in emotional stability confident are you about.” Athletes responded to each item associated with career choice, humor disorders, substance abuse, using a 5-point Likert scale varying from 1 (no confidence) unrealistic expectations regarding a sports-related career, and to 5 (total confidence). A confirmatory factor analysis (CFA) a lack of preparation for life and work outside of the sports of this scale provided an acceptable fit to the data as follows: world were also the consequences related to the high levels [χ2 = 354.92, DF = 54, SRMR = 0.021, Bollen–Stine (B–S) of AI (Brown et al., 2000; Lally and Kerr, 2005; Stambulova p = 0.032, Standardized Root Mean Square Residual and Root and Ryba, 2014). In fact, a previous study with Olympic Mean Square Error of Approximation (RMSEA) = 0.076 (90% athletes revealed that athletes with strong AI tend to not CI = 0.019, 0.143), Tucker–Lewis Index (TLI) = 0.969, and plan career retirement with consequences for life after sports Comparative Fit Index (CFI) = 0.990]. (Torregrosa et al., 2015). The intensity of AI varies in association with the past relationship to sports and current athletic Career Goals experiences, as well as the personal experiences of athletes Career Goal Setting scale (Wendling and Sagas, 2020) was used with athletic failures and successes (Horton and Mack, 2000). to assess the career goals of athletes. This scale is composed of However, this study also revealed that athletes with higher five items grouped into one factor (e.g., “I have a clear set of AI tend to reveal higher levels of self-efficacy, particularly goals for my career after sport”), preceded by the stem: “About Frontiers in Psychology | www.frontiersin.org 2 July 2021 | Volume 12 | Article 694868 Monteiro et al. Career Planning in Soccer FIGURE 1 | Hypothesized model to examine the role of self-efficacy, career goals, and athletic identity on career planning of elite soccer players. my career goals . . .” Athletes responded to each item using a 7- data as follows: [χ2 = 704.99, DF = 90, SRMR = 0.033, B–S point Likert scale, varied from 1 (totally disagree) to 7 (totally p = 0.118, RMSEA = 0.046 (90% CI = 0.001, 0.092), TLI = 0.931, agree). The CFA exhibited the following fit: [χ2 = 91.30, DF = 5, and CFI = 0.943]. SRMR = 0.025, B–S p = 0.019, RMSEA = 0.070 (90% CI = 0.052, 0.088), TLI = 0.958, and CFI = 0.986]. Data Collection Career Planning Written informed consent was obtained from soccer players. Approval for this study was granted by the Research Center The Career Planning Scale (Wendling and Sagas, 2020) was used in Sports Sciences, Health Sciences and Human Development to measure the career planning of athletes after sport. This scale (CIDESD), the institution that is registered in the Portuguese is composed of eight items, grouped into one factor (e.g., “I have National Science Foundation (FCT) under the reference a good understanding of the steps I need to take to reach my UID04045/2020. All procedures were under the 2013 Helsinki career goals”), preceded the stem: “About my career planning declaration and its later amendments. . . .” Athletes responded to each item using a 7-point Likert scale, The data were collected during the period from March which fluctuates from 1 (totally disagree) to 7 (totally agree). to September 2020. Each athlete received an e-mail with a The items 1, 6, and 7 are reverse-coded. The CFA revealed link to access the questionnaires using the Survey Monkey an adequate fit to the data as follows: [χ2 = 72.46, DF = 20, platform. Participants filled the multi-section survey in SRMR = 0.018, B–S p = 0.218, RMSEA = 0.054 (90% CI = 0.001, approximately 15 min. 0.126), TLI = 0.987, CFI = 0.996]. Athletic Identity Data Analysis The Portuguese version of the Athletic Identity Measurement Descriptive statistics were calculated for all variables under Scale (AIMS-plus Portuguese version) (Cabrita et al., 2014) was analysis. A two-step approach via the maximum likelihood used to assess the AI in line with the original AIMS (Brewer estimator method (ml) was employed in Amos 23.0 IBM SPSS. and Cornelius, 2001) and AIMS-plus (Cieslak et al., 2005). This (a) CFA was conducted to test the psychometric properties of questionnaire is comprised of 15 items distributed throughout the measurement model. Convergent validity, through average 5 subscales, namely, social identity, exclusivity, self-identity, variance extracted (AVE), was calculated, and scores ≥0.50 positive affectivity, and negative affectivity. The questionnaire were considered adequate (Hair et al., 2019). Discriminant contains a sliding-scale answer format, oscillating from 0 (totally validity was verified when the average variance factor of each disagree) to 10 (totally agree), thus, partial scores may be factor was less than or equal to the square correlations across calculated for each dimension along with a total AI score. each construct underlying the measurement model (Hair et al., Higher scores indicate a greater level of identification with 2019). Finally, the composite reliability of each construct was the role of athletes. The CFA revealed an adequate fit to the calculated via the formula suggested by Raykov et al. (2016), and Frontiers in Psychology | www.frontiersin.org 3 July 2021 | Volume 12 | Article 694868 Monteiro et al. Career Planning in Soccer scores ≥0.70 were considered acceptable. (b) Structural equation Structural Model modeling (SEM) to test the model fit was performed. In addition, The results from structural model displayed a good fit to the standardized direct and indirect effects on the outcome variables data as follows: [χ2 = 1,927.79 (734), SRMR = 0.049; B–S were analyzed. Therefore, bootstrap resampling (1,000 samples) p ≤ 0.001, RMSEA = 0.042 (90% CI = 0.032, 0.052), TLI = 0.959, via bias-corrected 95% CI was used to assess the significance and CFI = 0.965]. of the direct and indirect effects. The magnitude of effects was Overall significant associations were observed among evaluated through the following suggestions by Cohen (2013): variables under analysis, specifically (a) self-efficacy displayed trivial (0–0.19), small (0.20–0.49), medium (0.50–0.79), and large a positive and significant effect on career goals, AI, and career (0.80 and greater). The traditional incremental and absolute planning, (b) career goals displayed a positive and significant goodness-of-fit indexes are as follows: CFI, TLI, RMSEA, and its effect with career planning and AI, and (c) AI displayed a respective CI (90%) were employed to test the model fit for both negative and significant effect with career planning. CFA and SEM, and the cut-off values suggested by Hair et al. Self-efficacy displayed an indirect positive and significant (2019) were assumed as follows: CFI and TLI ≥ 0.90 and SRMR effect with career planning and AI via career goals. Career and RMSEA ≤ 0.08. goals also displayed a positive and significant effect with career planning via AI. The observed effects varied from trivial to large (Table 2). RESULTS A prior analysis showed no missing values, neither univariate of DISCUSSION outliers nor multivariate emerged. The values of skewness and kurtosis (i.e., between −2 and +2 and −7 and +7, respectively) The aim of this study was to test a model examining the role revealed no deviations from univariate normality. However, the of self-efficacy on career goals and AI on career planning of Mardia coefficient for multivariate kurtosis exceeded the expected elite soccer players. It was a first attempt to integrate AI with value (>0.5). Thus, Bollen–Stine bootstrapping (2,000 samples) self-efficacy and career goals to further explain the process of was employed for subsequent analysis. Possible collinearity career planning. In general, the model revealed a good fit to data, diagnosis was tested by the variance inflation factor (VIF) and helping to reinforce the positive contribution of self-efficacy and the tolerance test assuming values <10 for VIF and <0.01 for career goals and the negative contribution of AI to the career tolerance test. Consequently, the results displayed that both planning of professional soccer players. in VIF and tolerance tests scores were below 10 and above More specifically, the results confirmed all the hypotheses, 0.1, respectively, guaranteeing the proper conditions to test the revealing that (a) self-efficacy positively predicted career goals, regression model. career planning, and AI, (b) career goals positively predicted career planning and AI, and (c) AI negatively predicted career planning. Thus, while self-efficacy and career goals acted as Descriptive Statistics mediating facilitators (Creed et al., 2013; Demulier et al., 2013; Descriptive results revealed high values (above midpoint) for Wendling and Sagas, 2020), the AI played as a mediating barrier all studied variables, and bivariate correlations exhibited a for career planning. significant pattern across all variables. The highest correlation In this study, we aimed to look into the individual resources value was observed between career goals and career planning that positively or negatively constraint the process of career (r = 0.87), while the lowest values were observed between self- planning (Lent and Brown, 2013; Wendling and Sagas, 2020). efficacy and AI (r = −0.29) (Table 1). Looking into each variable in the analysis, the results revealed that soccer players with higher self-efficacy seem to better define Measurement Model career goals and directly and indirectly career planning. The The analysis of the measurement model included self-efficacy, self-efficacy of players seems to contribute to a better definition career goals, career planning, and AI. Results showed the of objectives of career and consequently to clarify their career measurement model fit to the data as follows: χ2 = 1,927.79 plans for the future (Creed et al., 2013; Lent and Brown, 2013). (734), SRMR = 0.069, B–S p ≤ 0.001, RMSEA = 0.059 (90% Such results are in line with a previous study that showed that CI = 0.047, 0.070), TLI = 0.922, and CFI = 0.935). Additionally, the definition of career goals of players was positively related the measurement model revealed no problems of convergent to the quality of career retirement (Park et al., 2013; Debois (all factors presented scores above 0.50 in AVE). In terms of et al., 2015). Furthermore, career planning seems to be influenced discriminant validity, a slight problem was identified involving by the level of self-efficacy of players through the definition of the career goals and career planning factors, due to the square career goals and building plans, helping to trigger the confidence correlation between them are above the AVE of each factor. The of players in career self-management and decision-making. In remaining factors did not present discriminant validity problems. fact, as previously pointed out by Monteiro et al. (2020), more In addition, all of the constructs present an adjusted value of than only the definition of goals, the achievement of goals composite reliability since all of them are ≥0.70. Therefore, demonstrated strong relation with the age of career retirement the psychometric properties of the measurement model were and also the level of retirement. However, the most important guaranteed (Table 1). issue is not to achieve a high level of performance but to maintain Frontiers in Psychology | www.frontiersin.org 4 July 2021 | Volume 12 | Article 694868 Monteiro et al. Career Planning in Soccer TABLE 1 | Descriptive statistics, bivariate correlations, average variance extracted, and composite reliability coefficients. Variables Range M SD 1 2 3 4 AVE CR 1. Self-efficacy 1–5 4.32 0.64 0.57 0.88 2. Career goals 1–7 5.53 0.88 0.34** 0.50 0.74 3. Career planning 1–7 5.24 0.71 0.27** 0.87** 0.50 0.79 4. Athletic identity 1–10 8.6 0.94 −0.29** 0.33* 0.42** 0.54 0.82 M, mean; SD, standard deviation; AVE, average variance extracted; CR, composite reliability; *p < 0.05; **p < 0.01. TABLE 2 | Direct and indirect regression paths. Regression path Direct Regression path Indirect β 95% CI p β 95% CI p SE → CG 0.24 [0.132; 0.388] 0.001 SE → CP 0.16 [0.091; 0.188] 0.002 SE → AI 0.22 [0.127; 0.334] 0.001 SE → AI 0.35 [0.242; 0.440] 0.001 SE → CP 0.41 [0.263; 0.563] 0.001 CG → CP 0.18 [0.063; 0.347] 0.001 CG → CP 0.83 [0.626; 0.881] <0.001 – – – – CG → AI 0.28 [0.162; 0.425] 0.001 – – – – AI → CP –0.32 [−0.468; −0.203] 0.002 – – – – SE, self-efficacy; CG, career-goals; CP, career planning; AI, athletic identity; β, standardized coefficient; 95% CI, confidence interval at 95%; p, level of significance. such level over the career. The feeling of self-achievement in these financial support and social status (Demulier et al., 2013; Martin cases seems to be crucial to maintain high levels of self-efficacy et al., 2014) and to the lack of self-awareness and planning about and a better definition of career planning (Demulier et al., 2013; the advantage to prepare the other career when the sports ends. Torregrosa et al., 2015). Moreover, Cabrita et al. (2014) showed that AI could constraint Looking into the relationship among self-efficacy, career goals, the choices or opportunities for post sports career, being this and AI, according to our model, the results revealed that players a primary determining factor in whether an athlete chooses a with high self-efficacy revealed a better definition of career goals sport-related career. and directly and indirectly increased the level of AI. The results According to our model, the career goals could act as a were in line with a previous study that revealed that players with mediator variable among sell efficacy, AI, and career planning. a strong self-efficacy tend to reveal a strong sense of AI (Lamont- That is, the definition and management of objective and Mills and Christensen, 2006). During athletic career, high levels achievable career goals for the development and also to the end of AI contributed to soccer players exclusively maintain the focus of career could ensure a more balanced and planned career on sports performance and on the management of professional development and retirement. Particularly, the identification and activities (e.g., training seasons, game schedule, request rest, link between career goals and life goals could be assumed of and pregame stages) (Torregrosa et al., 2015). That is, with the particular interest to decrease the level of AI, promoting the increase in self-efficacy and achievement of goals, soccer players process of career planning and career retirement (Torregrosa become more confident and tend to develop their thinks and et al., 2015; Stambulova et al., 2020). Despite the lack of feels around the sports world (Creed et al., 2013; Demulier et al., time and energy that athletes need to engage in non-sports- 2013; Wendling and Sagas, 2020). Also, the level of AI seems related activities (López de Subijana et al., 2015; Ekengren to be positively associated with athletic performance, a desirable et al., 2018; Stambulova and Wylleman, 2019), it seems a quality encouraged by coaches and structure of clubs (Martin key issue to decrease the level of AI and consequently allows et al., 2014). However, the major issue is that, as previously a better career planning process. Recently, the International demonstrated, at the moment of career retirement, the high Society of Sport Psychology assumed that to improve the levels of AI tend to negatively impact the process and quality of AI foreclosure in athletes, there is a need to create new retirement (Park et al., 2013). possibilities for the self-exploration of multidimensional personal In line with that, and according to our expectations, the identities and the development of a dual career of athletes results highlighted a negative relationship between AI and career (Stambulova et al., 2020). Thus, based on our results and planning. As observed with Olympic athletes (Torregrosa et al., in line with the holistic athletic career model (Wylleman 2015), soccer players (Carapinheira et al., 2019), handball players et al., 2004), it is suggested that the career management of (Ekengren et al., 2018), and student athletes (Lally and Kerr, elite athletes should start with the definition of career goals 2005; Wendling and Sagas, 2020), a strong AI is related to through the definition of some key performance indicators that a lack of career planning. The behavior of elite athletes, and help soccer players and managers to identify the achievement particularly the elite soccer players, to delay “undressing the of goals (Monteiro et al., 2020) followed by the definition sports sweater” could be related to the need to maintain the of parallel life goals and exploration of a dual career Frontiers in Psychology | www.frontiersin.org 5 July 2021 | Volume 12 | Article 694868 Monteiro et al. Career Planning in Soccer perspective in order to decrease AI and improve the career of a dual career project that allows decreasing the level of AI of planning (Torregrosa et al., 2015). players. The definition of career goals and the integration of life In summary, the proposed model demonstrated the mediating goals in the process of career management seems to be crucial for positive influence that career goals have on the relationship career planning and consequently for the process of retirement. among self-efficacy, career planning, and AI. These findings reinforce the important role of self-efficacy in this process and reinforce findings about the decision-making process on DATA AVAILABILITY STATEMENT sports career retirement (Park et al., 2013). Although this study contributes to the understanding of the role of self-efficacy, career The raw data supporting the conclusions of this article will goals, and career planning as well as AI in active soccer players, it be made available by the authors, if required and with some has some limitations. All the variables were assessed at the same restrictions considering its individual nature. time, so we can only address the associations among the variables without determining causality. In this sense, longitudinal or experimental studies are necessary to further examine the effects ETHICS STATEMENT of the studied variables. In addition, future studies should try The studies involving human participants were reviewed and to examine the respective effects of each studied variable and approved by Ethics Committee by the Research Center in possibly to examine whether these effects are invariant for soccer Sports Sciences, Health Sciences and Human Development players of different genders and different ages. It is possible that (CIDESD). The patients/participants provided their written studied variables may play different roles for male or female informed consent to participate in this study. players or that their effects may fluctuate according to the variations in their educational, social, or financial status. Finally, in this study, we included active soccer players, and future studies AUTHOR CONTRIBUTIONS should also explore other sports (e.g., futsal) and assess the perspective of soccer players after their career retirement. RM, DM, MT, and BT contributed to the conception and design Despite the positive interactions with self-efficacy and career of the study. RM and DM conducted the statistical analyses goals, AI revealed a negative relationship with career planning. and wrote the first draft of the manuscript. MT and BT wrote Thus, managers, coaches, and sports psychologists should start several sections of the manuscript. All the authors contributed to since the beginning of careers of soccer players to encourage the the manuscript revision, and read and approved the submitted definition of realistic career and life goals as well as the promotion version of the manuscript. REFERENCES Cohen, J. (2013). Statistical Power Analysis for the Behavioral Sciences. Cambridge, MA: Academic press. Alfermann, D., and Stambulova, N. (2007). “Career transitions and career Creed, P., Buys, N., Tilbury, C., and Crawford, M. (2013). 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Stambulova, N., Alfermann, D., Statler, T., and Côté, J. (2009). ISSP position stand: career development and transitions of athletes. Int. J. Sport Exerc. Psychol. 7, Copyright © 2021 Monteiro, Monteiro, Torregrossa and Travassos. This is an open- 395–412. doi: 10.1080/1612197X.2009.9671916 access article distributed under the terms of the Creative Commons Attribution Stambulova, N., and Ryba, T. V. (2014). A critical review of career research and License (CC BY). The use, distribution or reproduction in other forums is permitted, assistance through the cultural lens: towards cultural praxis of athletes’ careers. provided the original author(s) and the copyright owner(s) are credited and that the Int. Rev. Sport Exerc. Psychol. 7, 1–17. doi: 10.1080/1750984X.2013.851727 original publication in this journal is cited, in accordance with accepted academic Stambulova, N., Ryba, T. V., and Henriksen, K. (2020). Career development and practice. No use, distribution or reproduction is permitted which does not comply transitions of athletes: the International Society of Sport Psychology Position with these terms. Frontiers in Psychology | www.frontiersin.org 7 July 2021 | Volume 12 | Article 694868