Early Education Care from Its Practitioners to Achieve Sustainability

Abstract

(1) Background: Early intervention professionals are involved in the reconceptualisation of their service due to the exceptional situation caused by the COVID-19 epidemic, within the family context and aware of the children’s needs, with an impact on their emotional well-being to ensure sustainability. An analysis of their socio–emotional profile and training is increasingly needed to face their professional development effectively; (2) Methods: In this study, 209 early intervention professionals participated (n = 209), with an average age of 37.62 (±9.02). The following instruments were used: Satisfaction with Life Scale (SWLS), Wong Law Emotional Intelligence Scale (WLEIS-S) and the Utrecht Work Engagement Scale (UWES-9). The purpose of the study was to examine the relationship between early intervention (EI) and engagement as predictors of greater life satisfaction using Structural Equation Modelling (SEM). (3) Results: There exists a relationship between some dimensions of the instruments used (p < 0.01). The model obtained good structural validity (χ² = 3.264; Root Mean Square Error of Approximation (RMSEA) =.021; Goodness-of-Fit Index (GFI) = 0.991; Comparative Goodness of Fit Index (CFI) = 0.999; Incremental Fit Index (IFI) = 0.999). Subsequently, the results described above were verified through Bayesian statistics, thereby reinforcing the evidence provided; (4) Conclusions: Findings highlight the importance of providing professionals with emotional tools and strategies, from the educational context, in order to carry out their activity effectively and ensure the sustainability within the current situation, while remaining fully engaged.

1. Introduction

Currently, early intervention services (hereinafter EIS) represent the logical evolution of a model centred on the family context and focused on the needs of children from 0 to 6 years with developmental disorders or at risk of suffering them [1,2], with the aim of responding as soon as possible to their temporary or permanent needs in the early years [3,4]. Such actions should be planned by emotionally competent professionals and agreed with the family. Therefore, suitable strategies are generated by all the agents involved and it contributes to the comprehensive development of the child [5], thereby supporting shared working alliances, which is necessary and useful to improve the context where the intervention is carried out. Consequently, an outpatient and rehabilitative model is overcome to give way to a close, relational, educational, shared, dynamic and cooperative model, thus reorienting the intervention process towards a participatory practice with all agents involved [6].

The rapid spread of the virus known as COVID-19 led to the World Health Organization (WHO) declaring a global pandemic situation on 11 March 2020. The new restrictions that came with it were aimed at limiting physical contact [7], which had a significant impact on EIS providers.

The United Nations [8] establishes different educational proposals within the Sustainable Development Goals on which educational programmes for sustainable development should be based in order to change the world after the COVID-19 pandemic. This crisis has affected all five continents and, based on this global proposal, the United Nations considers that countries with more resources should help those with few resources; thus, following this proposal, the planet can be protected. Among the 17 priority Sustainable Development Goals [8,9] are: Sustainable Health and Well-being (3rd place), Quality Education (4th place), Gender Equality (5th place), Adequate Jobs and Economic Growth (8th place), Reducing Inequalities (10th place), Sustainable Cities and Communities (11th place), Climate Action (13th place), Life within the Earth (15th place), Peace, Justice and Strong Institutions (16th place) and Partnerships for the Goals (17th place). As can be seen, 10 out of the 17 goals considered are directly related to sustainable proposals and interventions. These proposals are based on action plans for people, planet and prosperity, which are also intended to strengthen global peace and access to justice. The member states of the United Nations adopted a resolution recognising that the greatest challenge in the world today is the elimination of poverty and declaring that sustainable development cannot be achieved without poverty eradication [9]. These strategies will be on the international agenda for the next 10 years in the economic, social and environmental spheres [8].

Multidisciplinary teams specialised in this area have been challenged to renew their educational practices, through a set of actions and promotion of practical and emotional strategies to involve the family. Consequently, families have positioned themselves as active agents within a common shared strategy in the child’s natural environment [10,11]. In order to achieve these objectives, EIS professionals have provided the necessary technological channels to support the family environment through reflection and shared search for educational solutions. This process has not been free of difficulties; some studies have been based on the need to assist parents when they receive an adverse diagnosis during the first years of their children’s life [12,13], and the co-responsibility to help them manage stressful situations and disaffection at this stage has even been corroborated [14,15]. Other studies have highlighted the need to establish affective ties of trust, search for personal resources and even an emotional relationship that fosters working alliances between professionals and families [1]. Due to the current situation, these issues have required professionals who are better prepared pedagogically, emotionally and motivationally, to be able to recognise the families’ feelings and address adverse situations throughout the process, both individually and professionally [10,16].

This is why concepts such as Emotional Intelligence (hereinafter EI) have been postulated as a necessary reference in the emotional and motivational development of EIS professionals, conditioned by different causes, which change and fluctuate among themselves, where individuals learn to manage or not, based on the evolution of their internal thinking and relationship with the context [16]. This model was developed theoretically in 1990, as personal ability to perceive, understand and regulate one’s own emotions and those of others adaptively [17,18] in their professional activity [19], and helps to know what emotional and intellectual qualities people possess [20].

Today, EI distinguishes between two relevant models: the EI ability model (cognitive-emotional), measured through tests of peak performance, and the mixed EI model (or emotional self-efficacy), measured through self-reported questionnaires [21]. The first, the ability model [22], focuses on the ability to process information through emotions to resolve conflicts adaptively [23,24], and its description is relevant to understanding internal processes and the acquisition of emotional competences [18,25,26,27]. A second approach, the mixed model [28], combines mental abilities with personality traits, and it is defined as the set of emotional abilities, personal and interpersonal motivations that will determine the interaction patterns related to external demands and pressures [29].

This research focuses its content on the mixed model, as it is one of the measures that has shown greater theoretical and empirical strength throughout the years when related to other variables such as engagement or work commitment, defined as positive state of mind and job satisfaction, characterised by vigour (willingness to devote effort to a specific action and persist when faced difficulties), dedication (commitment to the task and the feeling of pride and enthusiasm in it) and absorption (concentration on the action itself) of the individual in the work environment [30], which is stable over time and mediates between demands and work resources, providing positive effects on the results of the professional’s activity and job satisfaction [31].

It is important to note that the Demands and Resources at Work Theory (DRW), an extension of the Demands and Resources at Work Model [32], does not ignore the importance of negative states, such as job distress. Instead, a preventive approach is adopted in its content, by focusing on individual and organisational conditions that favour greater life satisfaction [33]. Different studies have found that professionals in the field of EIS who are committed to their work show greater satisfaction and less work stress due to their tendency to develop their activity involving their families. They also tend to show greater empathy and orientation towards the child’s progress when interventions are carried out through informal support [34,35], to guarantee the educational and comprehensive service quality offered [36,37,38]. This is a circumstance that has been compromised due to the current pandemic [39].

Another concept linked to professional commitment and development is life satisfaction [40], linked to subjective well-being and the prevalence of positive feelings over negative ones [41,42]. Life satisfaction becomes a key condition in the individual’s aspirations, where the emotional affects attributed to the management of success or failure have a direct impact on the development of professional activity [43,44]. Some works find this distinction useful in understanding subjective well-being and its relationship with EI [19,45,46].

In relation to this, the added challenge of the “new reality” in times of pandemic has increased the gap between ESI professionals, according to their socio–emotional profile. In these terms, those who have been able to redirect their professional activity virtually [47,48] and have shown high levels of EI and motivation are more effective in developing cognitive and emotional competencies, compared to those who have not been able to cope, with the latter showing greater dissatisfaction [20]. Hence, the acquisition of emotional competencies will be key not only professionally, but also in different life situations [49], during the pandemic period and, subsequently, in the reconstruction of the world as it has been known up to now [50].

Taking these considerations into account, this study is interested in the relationship between EI and engagement on life satisfaction during the COVID-19 pandemic, between each of the EI factors (Wong Law Emotional Intelligence Scale (WLEIS-S)) and engagement (Utrecht Work Engagement Scale (UWES-9)), as predictive values of greater life satisfaction, continuing previous studies where there is evidence of significant relationships between some of the variables of the instruments considered in ESI professionals [1,10].

Therefore, the general objectives of this research are: (a) to analyse the relationship between each of the components of EI, engagement and life satisfaction to achieve sustainability environments; (b) to analyse the parameters of EI, engagement and life satisfaction according to age in order to know if it is a variable that affects the establishment of sustainability; (c) to determine which variables of EI and engagement predict greater life satisfaction, through structural equation modelling (SEM) and Bayesian statistics.

In line with these research objectives, the present study will answer the following research questions:

-

How do the components of EI, engagement and life satisfaction of early care professionals relate to achieve sustainability?

-

Does the age of early care professionals affect the establishment of sustainability?

-

Which variables of EI and engagement best predict life satisfaction of early care professionals?

2. Materials and Methods

The present study is based on a quantitative cross-sectional and correlational analysis, where the exceptional circumstances and social impact of the global pandemic caused by COVID-19, according to the report on shared responsibility and global solidarity of the United Nations, are considered [8]. Based on these criteria, longitudinal, comparative and reliability measures were established through Cronbach alpha and Omega coefficient [50], also called Jöreskog Rho [51].

2.1. Participants

This study was conducted during the months of March to December 2020. According to the report on Shared Responsibility and Global Solidarity in Responding to the Social Impact of the Pandemic, the epidemic is not only a health crisis, but it is affecting fundamental principles of societies, especially those who are most vulnerable.

The research sample was drawn from a non-probabilistic intentional sample of early childhood professionals. We contacted 178 Child Development and Early Intervention Centres (CDEIC) from Spain, and 209 professionals voluntarily participated (n = 209), with an average age of 37.62 (± 9.02), with a range between 23 and 62 years. The professional profiles of the participants included 98 psychologists (46.9%), 50 speech therapists (23.9%), 3 physiotherapists (1.4%), 4 occupational therapists (1.9%), 5 psychomotor therapists (2.3%), and 18 others (8.6%). There was a majority of women, 190 women (90.9%) compared to 19 men (9.1%).

2.2. Instruments

The present investigation used four instruments validated in the Spanish context, all of them of renowned prestige. The intention was to use valid and reliable instruments that measure the multidimensional nature of the variables to be analysed, i.e., EI, engagement and life satisfaction.

Satisfaction with Life Scale. The Satisfaction with Life Scale—SWLS—[52] was used to assess life satisfaction. Specifically, we used the five-item version of the Satisfaction with Life Scale by Vázquez, Duque and Hervás [53]. The scale in the Spanish version reports an internal consistency of α = 0.82. The scale score reliability obtained in our study is α = 0.86 and the Omega coefficient obtained reported a value of ω = 0.78.

UWES-9. The Utrecht Work Engagement Scale, adapted to Spanish, in its reduced version of nine items, UWES-9 [54,55], was used to assess work engagement. This instrument is made up of three factors, representing the three core dimensions of work engagement: vigour (willingness to devote effort to a particular action and to persist in the face of difficulties), dedication (commitment to the task and pride and enthusiasm in it) and absorption (concentration on one’s own action). The nine items of the UWES-9 are scored on a Likert-type scale with seven response levels, ranging from 1 to 7. The reliability levels obtained in our study are: Vigour α = 0.77 and ω = 0.74; Dedication α = 0.82 and ω = 0.78; Absorption α = 0.72 and ω = 0.70.

WLEIS-S. The Spanish version of the Wong Law Emotional Intelligence Scale (WLEI-S) was used to assess EI [56]. It is based on the Wong and Law EI scale -WLEIS- [57], and it consists of 16 items and 4 dimensions: Intrapersonal Perception (appraisal of own emotions), Interpersonal Perception (appraisal of others’ emotions), Assimilation (use of emotions) and Emotional Regulation. A 7-point Likert-type scale (1 to 7 points) was used, with the reliability of the scores for the variable Assessment of own emotions being α = 0.85 and ω = 0.77; α = 0.86 and ω = 0.80 for the Assessment of others’ emotions; Use of emotions being α = 0.76 and ω = 0.71; and α = 0.88 and ω = 0.82 for Emotional Regulation.

2.3. Procedure

Participants were contacted by e-mail to different Child Development and Early Intervention Centres (CDEICs) located in Spain. Subjects were informed of the process to be followed, and the confidentiality and anonymity were ensured. For the administration of the instruments, a link to the instruments was provided, using the Google Form^® tool, in order to simplify the response through their mobile devices. For the fulfilment of the questionnaire, participants were given the option to ask any questions they might have via email. The ethical codes and guidelines of the Declaration of Helsinki [58] were also followed.

2.4. Data Analysis

To achieve a better adjustment for each test, the data were converted according to their factor loadings [59]. Descriptive statistics were obtained (means and standard deviations), and the reliability and internal consistency of each instrument was analysed in advance using Cronbach alpha and the Omega coefficient. We worked with the weighted sum of each variable, to overcome the limitations that could affect the proportion of variance [60] and the correlation between the resulting scores in each of the dimensions. Next, an analysis of mean differences by age was performed using the Kruskal–Wallis H-test for mean difference for unrelated samples. Non-parametric tests were used because the assumption of normality was not met in all cases based on the data obtained in the Kolmogorov–Smirnov test (n > 50 cases). In addition, the effect size in the analyses performed is reported. Next, to explore and quantify the predictive capacity of the EI and engagement variables on life satisfaction, a hierarchical multiple regression analysis was performed, subsequently removing those variables that were not included in the regression model. Finally, a Structural Equation Model (SEM) was developed with the purpose of evidencing the existence of significant differences between each of the variables of the evaluation instruments, and verifying the results through Bayesian statistics, which enables the quantification of evidence associated with the proposed model, and further strengthening of the final result. The implementation of the Bayes factor can be a great contribution to the inferential probabilistic analysis when addressing the different hypotheses, by contrasting the results obtained through classical statistics [60]. In all cases, a confidence level of 95% (significance p < ..05) was used, using IBM SPSS Statistics 25.0 (IBM, Chicago, IL, USA) and AMOS 25, to obtain the results of the tests indicated above.

3. Results

Based on the relational nature of each of the variables and taking into account that the normality criteria were not met, it was considered appropriate to use Spearman’s Rho correlation coefficient. Next, mean differences by age were performed to determine the incidence of each of the variables by intervals, and this procedure is appropriate when the data have a natural order.

Subsequently, a hierarchical regression model was developed, which is a fundamental statistical procedure for the development of our research, since it overcomes the dependence of the observed data, making it possible to overcome the limitations of classical regression methods. This procedure would not only have methodological implications in terms of results robustness, but could also be essential for the development of the structural equation model (SEM), in order to work only with those variables that were included in the regression model, while controlling the measurement error, following the logical order of the procedure.

3.1. Relationship between Life Satisfaction, Emotional Intelligence and Engagement

Table 1. Tables should be placed in the main text near to the first time they are cited.

Variable	α	ω	M (SD)	SV	SEA	OEA	UOE	ROE	VIGOUR	DED	ABS
LS	0.86	0.78	19.93 (±3.73)	-	0.33 **	0.24 **	0.36 **	0.35 **	0.37 **	0.39 **	0.31 **
SEA	0.85	0.79	17.24 (±2.60)		-	0.49 **	0.32 **	0.50 **	0.33 **	0.36 **	0.14 *
OEA	0.86	0.80	17.38 (±2.26)			-	0.29 **	0.47 **	0.28 **	0.29 **	0.20 **
UOE	0.76	0.71	16.15 (±2.57)				-	0.38 **	0.49 **	0.43 **	0.20 **
ROE	0.88	0.82	14.71 (±3.12)					-	0.47 **	0.37 **	0.17 **
VIGOUR	0.77	0.74	11.82 (±2.18)						-	0.75 **	0.39 **
DED	0.82	0.78	13.20 (±1.92)							-	0.45 **
ABS	0.72	0.70	19.93 (±3.73)								-

Note: (1) Mean = M; Standard deviation = SD; Life satisfaction = LS; Self Emotion Assessment = SEA; Other’s Emotions Assessment = OEA; Use of Emotions = UOE; Emotional regulation = ROE; Dedication = DED; Absorption = ABS (2) * = p < 0.05; ** = p < 0.01.

shows the correlation matrix scores (Spearman’s Rho, because it is a non-normal distribution), descriptive statistics (mean and standard deviation), analysis of the reliability of the scores (Cronbach alpha and Omega coefficient), and, in general, the reliability of the scores is appropriate.

The analysis of each of the dimensions from the instruments used reveals statistically significant relationships between Life Satisfaction and all the dimensions of the EI (WLEIS-S) and Engagement (UWES-9), with the highest correlation established with the dimensions Dedication (r(208) = 0.39; p < ..01) and Vigour (r(208) = 0.37; p < ..01). Similarly, there is a significant relationship between all the EI variables, with the highest value between the variables Self Emotion Assessment and Emotional Regulation (r(208) = 0.50; p < ..01). There is also a relationship between all the Engagement variables, with the highest value between Vigour and Dedication (r(208) = 0.75; p < ..01). Finally, there is a relationship between all the EI variables and Engagement, with the highest correlation established between Use of Emotions and Vigour (r(208) = 0.49; p < ..01).

3.2. Mean Differences According to Age

For age-related differences, three intervals were established (<35 years, 36–49 years and >50 years) using the non-parametric Kruskal–Wallis H-test. No significant differences were found in any of the dimensions of the instruments used (22 < 2.0; p > 0.05 ns). The effect size, Epsilon squared (E2), is small in all cases (See

Table 2. Mean differences according to age (Kruskal–Wallis H-test).

Variable	<35 Years	36–49 Years	>50 Years	χ2	p	Effect (E2)
	M (DT)	M (DT)	M (DT)
LS	19.68 (±3.81)	20.23 (±3.48)	19.80 (±4.37)	1.208	0.547	0.005
SEA	17.02 (±2.24)	17.37 (±2.56)	17.62 (±3.77)	4.866	0.188	0.023
OEA	17.54 (±1.69)	17.40 (±2.24)	16.67(±3.75)	0.283	0.868	0.001
UOE	15.90 (±2.50)	16.37 (±2.39)	16.28 (±3.37)	2.698	0.259	0.012
ROE	14.53 (±2.92)	14.79 (±2.91)	15.09 (±4.43)	1.664	0.435	0.008
VIGOUR	11.74 (±2.20)	11.93 (±1.93)	11.70 (±2.95)	0.250	0.882	0.001
DED	13.17 (±1.98)	13.26 (±1.76)	13.13 (±2.27)	0.210	0.995	0.001
ABS	11.89 (±2.30)	11.42 (±2.30)	11.25 (±2.40)	0.083	0.214	0.014

Note: (1) Standard deviation = SD; Life satisfaction = LS; Self Emotion Assessment = SEA; Other’s Emotions Assessment = OEA; Use of Emotions = UOE; Emotional regulation = ROE; Dedication = DED; Absorption = ABS (2) The effect size is expressed as the Epsilon squared value (E²).

).

3.3. Predictive Value of EI and Engagement on Life Satisfaction

Hierarchical regression models provide a key statistical resource for the development of our research by overcoming the dependence of the observations presented in their data and using only those variables that best fit and predict higher life satisfaction. In other words, this method is useful for estimating change and analysing the effects of predictors at several hierarchy levels [61], making it possible to overcome the limitations of classical regression methods, thus introducing an innovation for improving the quality of the analysis of studies of these characteristics. It would therefore not only have methodological implications in terms of strength of results but could also be essential from a practical point of view [62].

In order to explore and quantify the predictive ability of the EI and Engagement variables on life satisfaction, a hierarchical multiple regression analysis was performed, taking into account the distribution and variability of each factor [63], and subsequently we discarded those variables that were not included in the regression model, and verified the absence of multicollinearity problems (tolerance values < 0.20; IVF > 4.00), our values are between 1.135 and 1.757. The results of the Durbin–Watson test indicate that there is independence of errors, with the value of 2.119. Since it is between one and three, we accept the assumption.

The dimension included in the regression model explains 41.0% of the variance, with the EI variable assessment of one’s own emotions as the best predictor of life satisfaction (R = 0.503; Corrected R² = 0.172; F = 17.239), with the t-value also significant for the rest of the variables (See

Table 3. Multilevel regression analysis, criteria variable: life satisfaction.

Criteria Variable	R	R2	R2 Corrected	F	Predicting Variables	β	t
Life satisfaction	0.503	0.253	0.172	17.239
					UOE	0.181	2.557 *
					VIGOUR	0.180	2.248 *
					ROE	0.177	2.416 *
					ABSORPTION	0.163	2.524 *

Note: (1) Standard deviation = SD; Life satisfaction = LS; Self Emotion Assessment = SEA; Other’s Emotions Assessment = OEA; Use of Emotions = UOE; Emotional regulation = ROE; Dedication = DED; Absorption = ABS (2) * = p < 0.05.

).

3.4. Structural Equation Model (SEM)

Model fit was tested using the Chi-square (χ²), the Goodness-of-Fit Index (GFI) and the Root Mean Square Error of Approximation (RMSEA) as the absolute measure of fit. The corrected Goodness of Fit Index (AGFI), the Tucker–Lewis Index (TLI) and the Comparative Goodness of Fit Index (CFI) were used as measures of incremental fit. The Chi-square ratio (χ²) over the degrees of freedom (CMIN/GL) and the Akaike Information Criterion (AIC) were used as measures of parsimony fit [64].

First, the validity and adjustment of the model established from the data obtained in the hierarchical regression analysis was tested, and a significant associated Chi-square (χ²) value was found (χ² = 3.264; gl = 3; p =.001). However, this statistic is sensitive to sample size and should be interpreted with caution. For this reason, different studies recommend using other indicators to assess model fit [65]. Among the most commonly used, we highlight the Goodness of Fit Index (GFI), which presents a value of 0.991, thus indicating an acceptable model fit, as well as the value of the Comparative Fit Index (CFI), which obtains a value of 0.999. The Incremental Fit Index (IFI) value obtains an acceptable value of 0.999. The corrected Goodness of Fit Index (AGFI) has a value above 0.85, which also suggests a good fit. Finally, RMSEA indicates an anticipated fit with the total population value, and it is lower than 0.08 for the established parameters. The values of this index were proposed by Steiger and Lind [66], who suggested balancing the effect of model complexity by dividing by the number of freedom levels to test the model. Values below 0.08 are indicators of a good fit, where in our case it is 0.021. Consequently, the model fit is acceptable in relation to the data obtained.

Figure 1 shows the standardised weights between each of the variables included in the regression model, with a significance level of 0.005 (5% probability of error), for the highest regression weights of the variables below this value (see

Table 4. Standardised regression weights of the model.

Relationships between Variables			Estimations	R.W. E.E.	C.R.	p	S.R.W.
EI	<-->	Engagement	0.175	0.029	6.004	***	0.851
UOE	<---	EI	1.000				0.623
ROE	<---	EI	1.274	0.179	7.133	***	0.653
Absorption	<---	Engagement	1.000				0.807
Vigour	<---	Engagement	1.323	0.111	11.880	***	0.937
LS	<---	EI	1.257	0.638	1.968	***	0.673
LS	<---	Engagement	−0.164	0.438	−0.374	0.709	−0.113

Note: (1) Regression weights = R.W., Standardised regression weights = S.R.W., Error estimation = E.E., Critical Ratio = C. R. (2) Life satisfaction = LS; Self Emotion Assessment = SEA; Other’s Emotions Assessment = OEA; Use of Emotions = UOE; Emotional regulation = ROE; Engagement: Vigour; Absorption. (3) *** = p < 0.001.

), corresponding to EI and Engagement (6.004); EI and Emotional regulation (ROE) (7.133); Engagement and Vigour (11.880); Life Satisfaction and EI (1.968). The negative was Life Satisfaction and Engagement (−0.374).

In order to quantify the associated evidence of the data obtained, related to the proposed objectives, which would strengthen the use of classical statistics, Bayesian statistics was used, which is increasingly applied in scientific research [67] (see

Table 5. Standardised regression weights of the Bayesian model.

Relationships between Variables			Estimations	S.D.	p	Min	Max
EI	<-->	Engagement	0.181	0.031	***	0.125	0.246
ROE	<---	EI	1.285	0.192	***	0.952	1.711
Vigour	<---	Engagement	1.323	0.116	***	1.119	1.575
LS	<---	EI	2.091	2.709	0.153	2.568	7.089
LS	<---	Engagement	−0.699	1.798	0.105	−4.042	2.331

Note: (1) Standard deviation = SD (2) Life satisfaction = LS; EI = Emotional Intelligence; Emotional regulation = ROE; Engagement: Vigour. (3) *** = p < 0.001.

).

Although the concordance of the extracted data could be accepted in each of the classical criteria (p < ..05), the Bayes Factor (BF) offers both null and alternative hypothesis are equally likely and, by strengthening each of the estimated similar values, the trend is higher between Life satisfaction and Engagement in the Bayesian model (−0.699), compared to the classical model (−0.164). Similarly, a 95% confidence interval was obtained, which is different from the classical confidence interval.

Although the data obtained from Bayesian statistics cannot be generalised, there is sufficient evidence to strengthen the reported model [68,69].

Overall, the results revealed a statistically significant correlation between life satisfaction, EI and Engagement in EIS professionals, and no differences were found in relation to age. The dimensions of use of emotions, vigour, emotional regulation and absorption were the predictors of life satisfaction. Finally, structural equation modelling (SEM) was used as a confirmatory technique and, subsequently, using Bayesian statistics, proved to be ideal for structural analysis, corroborating each of the results found.

4. Discussion and Conclusions

This research corroborates the importance of the acquisition of emotional competencies by professionals when carrying out interventions within Early Attention EA, especially during the COVID-19 pandemic, as a priority objective for Sustainable Development. In other words, emotions, always involved in interpersonal relationships, have been affected by children with developmental disorders or at risk of suffering them during the confinement period [8,9].

The aim of this study was focused on determining which variables of EI and engagement may act as predictors of greater life satisfaction in early intervention professionals in Spain during the period of isolation due to the global pandemic of the virus known as COVID-19 [70], where health recommendations demanded physical and social separation as a preventive measure against the spread of the virus [7]. In general terms, the results are convergent with other studies [29,46,69,70,71], the influence of EI and engagement on life satisfaction is evident.

The reliability of each of the instruments was verified through the calculation of Cronbach alpha and subsequently the Omega coefficient, as this latter is the most appropriate estimation when there is disparity in the factor loadings of each item (Tau-Equivalence), by working with the weighted sum of each variable and overcome the limitations that could affect the variance ratio [60,72].

Regarding the first research question, the results showed a statistically significant correlation between life satisfaction and each of the EI and engagement variables, with the highest value found with the use of emotions (UOE) and Dedication. These results are consistent with other studies on the relationship between EI and engagement to achieve greater psychological well-being and life satisfaction [23,33,73]. That is, emotional perception and use are decisive in regulating a positive mood and becoming more satisfied with life [74]. Similarly, the work dedication of professionals who are engaged in their jobs and who also show a positive aptitude tend to feel more effective. These findings are consistent with previous studies, where engagement has been related to high perceived self-efficacy and high professional performance under the structural environmental changes caused by COVID-19 [75]. Therefore, EIS professionals who perceive and manage the emotions of families and children based on their previous knowledge and experience have been able to adjust their professional practice to the current situation.

As our results suggest, the acquisition of emotional competencies may be decisive to adaptive problem solving [25]. Direct attention to students requires close contact with them and their families, where the socioemotional skills of the professional become a guarantee of the quality of their professional activity [10]. Although the background data found directly link each of the EI and engagement dimensions with life satisfaction in EIS professionals, the findings presented in this study confirm the importance of emotional factors in coping with the current situation adaptively [20,76,77,78]. Emotional competences will allow individuals, EIS professionals, not only to commit themselves to the performance of their professional activity, but also to create a strong basis to support their performance in different areas of their lives successfully [79].

In relation to the second research question, that is, to understand the impact of the age of early care professionals to achieve sustainability, no differences were found with life satisfaction, although it is notably higher in all its dimensions in younger individuals. Familiarity with the use of technological resources by the youngest individuals to reshape their professional activity and more easily replace face-to-face educational interventions with virtuality [48], may be one of the reasons for their slight superiority with respect to those of older age. However, the widespread motivation that usually associates EIS professionals, their willingness to design and develop shared interventions with families [80], are reasons that explain their high life satisfaction [81]. To respond to the third research question on the EI and engagement variables that best predict the life satisfaction of early care professionals, a hierarchical regression analysis was performed, and those dimensions with non-significant values were discarded. In our case, the EI variables included in the model were: Emotion Use and Emotional Regulation; the engagement variables were: Vigour and Absorption. According to Kahn [82], the hierarchical regression model is one of the most common multivariate techniques that enable checking of which variables are significant in relation to the established model. This approach is reasonable and, based on these criteria, an analysis was developed with the multivariate statistical technique of structural equations to corroborate what was reported in the regression analysis, where the model presented a good fit regarding the effect of EI and engagement on life satisfaction, and this evidence was supported by Bayesian statistics. If we compare our findings with those of other similar studies, we confirm the importance of providing future EIS professionals with emotional strategies within the educational sphere, especially when working with early childhood [83]. These pre-learned qualities should directly affect the selection and implementation of actions used [84], thereby allowing them to regulate the stress generated in their professional development. In turn, the knowledge of these competencies directly affects the comprehensive development of the children with whom they work directly [85]. Regarding the negative relationship between engagement and life satisfaction, it is possible that it is determined by the current pandemic situation, where the effort has not been evidenced, and this has an impact on the mood and well-being of these professionals [86], the literature highlights the importance of strengthening the emotional well-being of these professionals to keep their commitment and dedication to carrying out their activity, while enabling them to face challenging situations that may threaten their socio–emotional balance [87].

Some limitations are noted. The different analyses were conducted during the exceptional period of isolation, due to the COVID-19 pandemic situation; therefore, the subjective functionality of the self-reported instruments may condition the data through social desirability mismatches. On the other hand, the sample size and current situation do not permit generalisation of the results to other contexts, so it would be useful in future research to explore the incidence produced during this period, adding value to the emotional competences acquired when they face threatening situations, which could generate growth from the experience. These limitations imply that the findings should be treated with caution, awaiting their contrast in future studies.

The research carried out provides keys to the relationship between three core constructs (Emotional Intelligence, Engagement and Life Satisfaction) that affect the effectiveness and socio–emotional well-being of EIS professionals. The current convulsive situation in the world society makes it necessary to professionalise those agents who work directly in the social sphere and, especially, with vulnerable groups such as early childhood. Once the important role played by socio–emotional training has been proven, in order to increase effectiveness in professional performance to achieve sustainability, while achieving greater life satisfaction and commitment, future studies should focus on examining the relationship between these constructs and other closely related ones, such as burnout.

Furthermore, further research will have to consider with greater precision the incidence in relation to the development of emotional competencies during the pandemic period in professional EIS practice, with the evidence reported as fundamental to clearly consider the link between the reorientation of the practice developed and the emotional competencies acquired.