Searching the core symptoms of perinatal bonding disorders: A series of taxometric analyses for three bonding scales

Searching the core symptoms of perinatal bonding disorders: A series of taxometric analyses for three bonding scales | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Searching the core symptoms of perinatal bonding disorders: A series of taxometric analyses for three bonding scales Ayako Hada, Yukiko Ohashi, Yuriko Usui, Kyoko Sakanashi, Tomoko Tanaka, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6858320/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background A bonding disorder refers to the impaired or disordered emotional bond of parent to child has been viewed as pathological: bonding disorder. However, the core symptoms of this disorder have not yet been identified. Aim This study aimed to identify the combination of symptoms that showed the greatest degree of taxonicity. Methods A taxometric analysis was conducted using three samples of cross-sectional data from previous studies. Study 1 included the Japanese version of the Mother-to-Infant Bonding Scale, Study 2 included the Postpartum Bonding Questionnaire, and Study 3 included the Scale of Parent-to-Child Emotions (SPCE). The input indicators for the taxometric analysis were selected by checking whether they met the requirements for the analysis. After performing the mean above minus below a cut (MAMBAC), MAXimum EIGen value (MAXEIG), and latent mode (L-Mode) analyses, the comparison curve fit index (CCFI) profiles were obtained when the taxometric results appeared categorical. Results The CCFI profiles (i.e., mean CCFI profiles of those showed above 0.550) for the combination of the Anger, Fear and Sadness of parent-to-child emotions measured by the SPCE suggested that the data were categorical. Conclusion The SPCE can be considered an attractive and effective assessment tool for measuring parental bonding in future research and clinical practice. (175 words) Perinatal bonding disorder taxometric analysis core symptoms Mother-to-Infant Bonding Scale Postpartum Bonding Questionnaire Scale of Parent-to-Child Emotions Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Determining whether sets of psychological symptoms are categorical or dimensional has a significant impact on the research, theory, and practice of psychiatry and psychology. Kendel and Brockington (1980) noted; If the term entity is to have any meaning at all in contemporary psychiatry, it should imply the existence of a natural boundary or discontinuity between the condition in question and its neighbours. To use the imagery of the old aphorism that classification is art of carving nature at the joints, it should imply that the there is indeed a joint there, that we are not sawing through bone. The most obvious way of doing this is to demonstrate, in a representative and unselected population, that patients exhibiting a mixture of the symptoms of the condition in question and those of neighbouring syndromes are relatively uncommon. The mixed forms, the greys, must be shown to be less common than the pure forms, the blacks and the whites, which in mathematical terms involves demonstrating that a distribution of scores on a linear variable, derived from the relevant symptoms, is bimodal with a ‘point of rarity’ in the middle, rather than unimodal. Once a categorical structure is identified for them, those symptoms are presumed to be qualitatively different from others. The cases belonging to a single categorical group indicating a pathology should be identified. However, if a categorical structure is refuted, these psychological symptoms are seen as continuous, differing only in the degree of severity and appearing to be pathological when the symptoms are severe. A drawback of the issue of ‘point of rarity’ is that a pathological entity usually comprises multiple rather than a single symptom (indicator). Thus, although the distribution of the severity of a symptom seems continuous (without the point of rarity), this may not be the case when looking at the multidimensional space using more than one symptom simultaneously. Therefore, a multivariate distribution should be considered. This is called as taxometrics technique (Meehl, 1965 1992 ; Ruscio et al., 2006 ; Waller & Meehl, 1998 ). A group that can be clearly distinguished from others is called a taxon, and taxa can be generated using a step function applied to quantitative variables (Waller & Meehl, 1998 ; Ruscio et al., 2006 ). Historically, an impaired or disordered emotional bond of parent to child has been viewed as pathological: bonding disorder (Brockington et al., 2001 ; Kitamura et al., 2015 ; Kumar, 1997 ). Severe bonding disorder leads to negative effects on the relationship between a parent and child dyad. For example, postnatal women with higher scores in bonding disorder showed deficiencies in positive parenting behaviours such as low affective sensitivity, low warmth, and low engagement and flexibility (Muzik et al., 2013 ). Such relationships negatively affect children’s development. Bonding impairment at 6–8 months after childbirth was associated with child developmental delay at 12–15 months postnatally (Faisal-Cury et al., 2021 ). Furthermore, a study using a cluster analysis of maternal bonding after childbirth (Matsunaga et al., 2017 ) reported that a cluster with a high score of the Japanese version of the Mother-to-Infant Bonding Scale (MIBS-J; Yoshida et al., 2012 ) indicating the risk of bonding disorders showed high scores of the psychological abuse subscale of the Parent–Child Conflict Tactics Scale (CTSPC: Straus et al., 1998 ). Postnatal bonding disorder, but not depression, was a predictor of neonatal abuse (Ohashi, Sakanashi et al., 2016 ). On the other hand, high quality of maternal bonding contributes to better infant development outcomes (Le Bas et al., 2019). Nevertheless, the symptomatology and nosology of bonding disorders remain unclear. Despite an overwhelming amount of research on the causes, risk factors, and consequences of bonding disorders (e.g., Branjerdporn et al., 2017 ; Røhder et al., 2020 ; Trombetta et al., 2021 ), few studies have contributed to the concept and nosology of the parental bonding disorder. What symptoms should be included under the rubric of bonding disorder? No consensus exists on the diagnostic criteria or the definition of bonding disorder based on symptomatology or nosology. Bonding disorder researchers included different types of symptoms under the rubric of bonding disorder. They include emotional (e.g., ‘I am fond of my baby’, ‘I hate my baby’), motivational (e.g., ‘I want to protect my baby’), and behavioural (e.g., ‘I feed my baby with joy’, ‘I hit my baby’) symptoms (indicators). Identifying the core symptoms of bonding disorders is important in research and clinical settings. Many instruments have been used to measure parental bonding and bonding disorders, and almost all of these measures have a multiple factorial structure. The MIBS-J had a 2-factor structure (Kitamura et al., 2015 ; Yoshida et al., 2012 ). The Postnatal Bonding Questionnaire (PBQ; Brockington et al., 2001 ) had a 3-factor structure (Matsunaga et al., 2021 ; Ohashi, Kitamura et al., 2016 ). The Scale of Parent-to-Child Emotions (SPCE; Hada et al., 2024 ), which is a new scale for measuring parents’ bonding emotions, has 9 domains. Thus, the symptoms of bonding disorder can be explained by combining several symptoms. Nosology begins with the dissection, examination, and identification of abnormalities in organs and tissues visible to the naked eye. It distinguishes between unhealthy and healthy tissues. The same applies to invisible psychological symptoms. The identification of pathological symptoms is likely to be useful for classification. This involves two important issues: (a) which symptoms should be included in bonding disorder and (b) what are the core symptoms of bonding disorders? These are interdependent. Whether we can identify a taxon largely depends on which symptoms (indicators) are included under the rubric of the entity in question. A categorical taxon can be identified only when we include ‘core’ symptoms (indicators) under such an entity to be entered into a taxometric analysis. In other words, ‘core’ symptoms mean those that can identify a taxon. Demonstrating the dimensionality of an entity (using a specific number of indicators) may not necessarily be a proof of lack of taxonicity because a different combination of symptoms (indicators) may prove the existence of a taxon. Thus, dimensionality is a ‘null hypothesis’ in taxometrics. The null hypothesis is proven only when repeated trials (using different combinations of symptoms) fail to prove the alternative hypothesis. Furthermore, the proof of taxonicity is also categorical and not a matter of degree. In this study, we used three sets of data in which different bonding disorder measures—MIBS-J, PBQ, and SPCE—were used among Japanese postnatal women to explore the taxon of the pathology of parent-to-child bonding, which has core bonding disorder symptoms. In the third part using the SPCE data, we compared different combinations of parent-to-child emotions so that we could identify the combination that showed the greatest degree of taxonicity. RESEARCH DESIGN This exploratory research used a taxometric analysis consisted of three parts. Three cross-sectional data samples from previous studies were analysed in the same statistical manner. The details of these samples are described in each subsequent section. Study 1: MIBS-J The MIBS-J is well known and is the most widely used tool for detecting postpartum bonding disorder in clinical settings in Japan. The MIBS was originally based on the Mother Infant Bonding Questionnaire (MIBQ: Kumar, 1997 ), which was selected from the mother’s narrative accounts. Both MIBQ and MIBS were developed to screen the general population for problems in the mother’s feelings towards her new baby. Several studies have validated the MIBS-J for clinical use (Hashijiri et al., 2021 ; Kitamura et al., 2015 ; Matsunaga et al., 2017 ; Yoshida et al., 2012 ). Methods Participants The data were obtained from a questionnaire survey on postpartum depression at two time points (Time 1 at 5 days and Time 2 at 1 month after childbirth) (Baba et al., 2017 ; Hada et al., 2019 ; Matsunaga et al., 2017 ; Ohashi, Takegata et al., 2016). The survey was conducted between August 2001 and April 2002. Women who gave birth were recruited at five obstetric clinics in Okayama, Japan. Women who were not fluent in Japanese were excluded. Approximately 1,530 women were eligible for the study, of whom 1,200 (78%) received the questionnaires and 758 (63%) of those returned the questionnaires at both time points. Participants with missing values in the MIBS were excluded, and 723 (95%) participants were included in the sample. The participants’ mean (standard deviation [SD]) age was 28.7 (4.1) years; 444 (58.6%) participants already had already a child. We used the sample of mothers who had given birth a month after childbirth. Measurements Japanese version of Mother-to-Infant Bonding Scale (MIBS-J; Yoshida 2012) The MIBS-J is a scale consisting of 10 items rated a 4-point Likert scale (0 = ‘not at all’ to 3 = ‘very much’). The items reflect the mother’s feelings towards her infant. Higher scores indicate worse mother-to-infant bonding. A factor analysis of the MIBS-J was reported indicating a 2-factor structure: Anger and Rejection (AR) and Lack of Affect (LA) (Kitamura et al., 2015 ; Yoshida et al., 2012 ). Furthermore, a three-item structure consisting of Item 1 (‘I feel loving towards my child’; loving), Item 6 (‘I enjoy doing things with my child’; enjoying), and Item 8 (‘I feel protective towards my child’; protective) was accepted for strict invariance across several time points after childbirth (Baba et al., 2023 ; Yamamoto et al., 2023 ). Statistical analysis Taximetrics is a powerful analytical technique to determine whether a construct of interest is categorical or dimensional. The taxometric methodology is characterised by the use of several mathematically independent procedures. Taxometric methods include the mean above minus below a cut (MAMBAC; Meehl & Yonce, 1994 ), MAXimum COVariance (MAXCOV; Meehl & Yonce, 1996 ), MAXimum EIGen value (MAXEIG; Waller & Meehl, 1998 ), and latent mode (L-Mode; Waller & Meehl, 1998 ) analyses. The comparison curve fit index (CCFI) examines whether the empirical data are closer to taxonomic or dimensional comparison data, which is useful for specifying and evaluating different operationalisations of consistency testing. The values of the CCFI ranged from 0 (strongest support for the dimensional structure) to 1 (strongest support for the taxonic structure), with a value of 0.50 representing the most ambiguous result (Ruscio et. al., 2010 ). The CCFI profile is a summary index of the CCFIs employed in a series of taxometric analysis procedures (i.e., MAMBAC, MAXEIG, and L-Mode) (Ruscio et al., 2018 ). Indicator selection A taxometric analysis is required to meet several recommendations: (a) a sample size of 300 or more (Meehl & Yonce, 1996 ), (b) three or more indicators for MAXEIG and L-Mode (Waller & Meehl, 1998 ), (c) four or more ordered categories in indicators, (d) each indicator discriminates between putative taxa and complement groups at d ≥ 1.25, and (e) within-group correlations for putative taxa and complement between indicators do not exceed 0.30. We examined the mean, SD, skewness, and kurtosis of the candidates as input indicators for the taxometric analysis. We then examined whether the candidate variables were validated for taxometric analysis in terms of Cohen’s d and within-group correlations for the putative taxon and putative complement before conducting the taxometric analysis. We set the base rate of the putative taxon to the prevalence rate of bonding disorders measured by each measurement to check whether the candidate variables were suitable for taxometric analysis. This is because cases can be assigned to putative groups based on prior theories, diagnostic criteria, or conventionally applied thresholds (Ruscio et al., 2011 ; Ruscio & Wang, 2022 ). The goal for item selection in the taxometric analysis was to select 3 indicators for each measurement. As the meanings of the contents and constructs of each measurement of bonding were subtly different, indicators were needed to reflect these differences as symptoms. Therefore, it was necessary to consider which symptoms are core. The procedure of indicator selection for the taxometric analysis is shown in Fig. 1. If a measurement had three subscales, we used the three subscale scores as input indicators. The composite variables can be candidate input indicators for taxometric analyses. One advantage of forming composite variables is that the resulting input indicator may comprise a larger range of variables, providing a more reliable rank ordering of the cases (Ruscio et al., 2011 ). If a measurement has more than three subscales, all combinations of three subscales selected from all subscales were examined in terms of Cohen’s d and within-group correlations. The combination of three subscales that meet the criteria of Cohen’s d and the mean within-group correlation not exceeding 0.30 were selected as input indicators for the taxometric analysis. While datasets should meet each of these five recommendations (above a to e), several simulation studies have shown that boundary values on some of these criteria or a failure to meet one or more criteria may be counterbalanced by the favourable characteristics of other criteria in the same datasets (Ruscio et al., 2011 ). Therefore, we selected all combinations indicating a Cohen’s d > 1.25 and mean within correlations < 0.30 among all combinations of each of the 3 subscales for the input indicator. INSERT FIG. 1 AROUND HERE MAXEIG, MAMBAC, and L-Mode analyses Taxometric analyses were performed. First, MAMBAC, MAXEIG, and L-Mode were performed using the RunTaxometrics() function in the Rtaxometrics package. The number of cuts along the input variable (i.e., n.cut parameter) was set to 50 cuts, and the number of cases at each extreme along the input variable before making the first and last cuts (i.e., n.end parameter) was set to 25 for the MAMBAC analysis. The number of overlapping windows was set to 50, and the proportion of overlap between windows was set to 0.90 in the MAXEIG analysis. The search for the left mode beyond 0.001 and the right mode beyond 0.001 was included in the L-Mode settings. We set the base rate of the putative taxon to the prevalence rate of bonding disorders measured by each measurement to be the same as that in the indicator selection process. Because approximately 15% of women had bonding disorders after childbirth (Matsunaga et al., 2017 ), we set the base rate of the putative taxon as 0.15 in the taxometric analysis. Generating CCFI profiles After performing MAMBAC, MAXEIG, and L-Mode, we examined the CCFI profiles using the RunCCFIprofile() function. When the taxometric results appear categorical, one may wish to estimate the relative sizes of the two groups, called taxon and complement. The relative sizes of the two groups are shown by taxon-base rate estimates and can be calculated using the CCFI profiles (Ruscio et al., 2018 ). Examining the CCFI profile, which is a plot of CCFIs by taxon base rates, provides more accurate base rate estimates when the data appear to be categorical and offers clearer results when the data structure is ambiguous (Ruscio & Wang, 2022 ). We performed a taxometric analysis with CCFI profiles when the mean CCFI exceeded 0.45 because the ambiguous CCFIs ranged from 0.45 to 0.55 (Ruscio et al., 2018 ; Ruscio & Wang, 2022 ). The Rtaxometrics package (Ruscio and Wang, 2021 ) was used for all taxometric analyses. Results Indicator selection First, we determined which subscales or items should be used as input indicators for the taxometric analysis. The MIBS-J was confirmed in only two subscales using Japanese samples (Yoshida et al., 2012 ; Kitamura et al., 2015 ). Therefore, candidates for the input indicators of the MIBS-J for taxometric analysis were selected from its items. Because the MIBS-J has three stable (invariant) items (Items 1, 6, and 8) (Baba et al, 2023 ; Yamamoto et al, 2023 ), we considered these items as the input indicators for the taxometric analysis of the MIBS-J. Among the MIBS-J items, although Item 8 had a high kurtosis (6.82), the skewness and kurtosis of all other items indicated normal distributions (Table 1). INSERT TABLE 1 AROUND HERE Second, we checked Cohen’s d between and within-group correlations for the putative taxa and complements of the 3 items (Items 1, 6, and 8) of the MIBS-J (Table 2). Within-group correlations for the putative taxa and complements for the MIBS-J were -0.29 and 0.12, respectively. All Cohen’s d were greater than 1.25. Finally, the 3 indicators (items 1, 6, and 8) were used for the taxometric analysis of the MIBS-J. INSERT TABLE 2 AROUND HERE MAXEIG, MAMBAC, and L-Mode analyses The MAMBAC, MAXEIG, L-Mode, and mean CCFIs for the MIBS-J are shown in Table 3. The mean CCFI of the MIBS-J was 0.585. INSERT TABLE 3 AROUND HERE Generating CCFI profiles Because the MIBS-J data appeared ambiguous or categorical rather than dimensional, we generated the CCFI profiles. All CCFI profiles of the MIBS-J were below 0.500, and the mean CCFI profile was 0.427 (Table 4). INSERT TABLE 4 AROUND HERE Discussion The findings of Study 1 showed that the three symptoms of bonding disorder (loving, enjoying, and protective) measured by the MIBS-J were likely to be dimensional. Even if a woman does not feel loving, enjoying, or protective towards her child, such symptoms are likely to vary in terms of degree. Although clinicians have paid attention to experiencing a delay in the onset or loss of maternal emotional responses, such as loving, enjoying, and protecting the infant (Brockington et al., 2006 ), these symptoms may not be the core of discrete bonding disorder . The three indicators used in Study 1 (loving, enjoying, and protective) were invariant across measurement occasions (Baba et al., 2023 ; Yamamoto et al., 2023 ) and did not endorse the core in terms of taxonicity. However, this finding does not necessarily refute the possibility that different combinations of symptoms (indicators) indicate the existence of a taxon. Study 2: PBQ The PBQ is also well-known as a scale for detecting bonding disorder worldwide as well as in Japan. The PBQ was validated in a study of mothers who were referred for emotional or psychological issues. However, because of its large number of items (25), the PBQ is unlikely to be preferred over the MIBS-J in clinical situations. Methods Participants The data was obtained from three time points across the phases of childbirth (Time 1 during pregnancy, Time 2 at 5 days after childbirth, and Time 3 at 1 month after childbirth). We recruited pregnant women of at least 28 weeks’ gestation who attended antenatal clinics during the entire month of November 2011 ( N = 1,450). A set of questionnaires was distributed to these women during late pregnancy and at 5 days (while in the hospital) and 1 month (while attending the one-month health check-up) after childbirth. We focused on the symptoms of bonding disorder which were measured using the PBQ. For a taxometric analysis, a sample size of 300 or more is recommended (Meehl & Yonce, 1996 ). Therefore, we used data from the Time 2 sample ( n = 418), excluding participants with missing values on the PBQ. These participants’ mean (SD) age was 30.2 (4.7) years, and among them, 245 (58.6%) already had another child/chidren. Measurement Postpartum Bonding Questionnaire (PBQ; Brockington et al., 2001 ) The PBQ is a scale consisting of 25 items reflecting a mother’s feelings (e.g., ‘I feel angry with my baby’), cognitions (e.g., ‘My baby cries too much’), attitudes (e.g., ‘I feel like hurting my baby’), and behaviour (e.g., ‘I have done harmful things to my baby’). These items are scored from 0 to 5. Higher scores indicate more negative feelings, cognitions, or attitudes towards the infant. The factor structure of the Japanese version of the PBQ has a 3-factor structure: Anger and Restrictedness (AR), Lack of Affection (LA), and Rejection and Fear (RF) (Matsunaga et al., 2020; Ohashi, Kitamura et al., 2016 ). Statistical analysis The statistical analysis used in this study was the same as that used in Study 1. We set the base rate of the putative taxon to the prevalence rate of bonding disorder measured by each measurement to be the same as in the indicator selection process. There were no data on the prevalence rate of bonding disorder, as measured by the PBQ, in Japan. Therefore, we set the base rate of the putative taxon as 0.15 in the taxometric analysis of the symptoms of bonding disorder for the PBQ, similar to the MIBS-J. Results Indicator selection As the PBQ has 3 subscales, these subscale scores were the candidate indicators for the taxometric analysis. The mean, SD, skewness, and kurtosis of all input indicators for the taxometric analysis of the PBQ were calculated (Table 5). Among the PBQ subscale scores, the kurtosis of all subscales was high, particularly for RF. INSERT TABLE 5 AROUND HERE As the candidates for the input indicators of the PBQ were 3 subscales (i.e., AR, LA, and RF scores), we checked Cohen’s d between and within-group correlations for the putative taxa and complements of those subscale scores (Table 6). Within-group correlations for the putative taxa and complements of the PBQ subscales were 0.25 and 0.11, respectively. All Cohen’s d values were greater than 1.25. Finally, 3 indicators (AR, LA, and RF) were used in the taxometric analysis. INSERT TABLE 6 AROUND HERE MAXEIG, MAMBAC, and L-Mode analyses The MAMBAC, MAXEIG, L-Mode, and mean CCFI values for each scale are listed in Table 7. The mean CCFI of the PBQ was 0.512. INSERT TABLE 7 AROUND HERE Generating CCFI profiles Because the PBQ data appeared ambiguous rather than dimensional, we generated the CCFI profiles. All CCFI profiles of the PBQ were below 0.500 (Table 8). The mean CCFI profile was 0.446, indicating data dimensionality. INSERT TABLE 8 AROUND HERE Discussion Study 2 showed that the symptoms of bonding disorder (AR, LA, and RF) measured by the PBQ were likely dimensional in nature. The PBQ was originally developed to detect severe bonding disorder cases, such as an abusive parent, in comparison to other scales, including the MIBS. Thus, items such as ‘I have done harmful things to my baby’ or ‘I feel like hurting my baby’ that indicated severe symptoms of bonding disorders were included in the PBQ; this was described as pathological anger or established rejection towards one’s own baby (Brockington et al., 2006 ). However, these symptoms did not have a categorical structure. Those symptoms may not be at the core in terms of the taxonicity. A different combination of symptoms (indicators) may indicate the presence of taxa. Study 3: SPCE Methods Participants The sample was obtained from our cross-sectional web survey, which aimed to validate the SPCE (Hada et al., 2023). The participants in this study comprised 780 men and 780 women who were first-time parents and whose child’s age ranged from being a foetus to 12 years old. This survey was conducted in cooperation with Cross Marketing Inc. (Shinjuku, Tokyo, Japan) in 2022. Information from a web questionnaire was sent via e-mail to the premise-targeted individuals within the research panels of Cross Marketing Inc. In this study, 729,559 eligible people were estimated to be the recruit premise targets. The response rate was 9.23–13.23%. We used only women data. As the SPCE had strict measurement equivalence using the item response theory, the stability of its construct was assured across the children’s ages. The participants’ mean (SD) age was 34.3 (8.3) years. Measurement Scale of Parent-to-Child Emotions ( SPCE; Hada et al., 2023 ) The SPCE is a scale used to measure parents’ primary emotions with respect to their child. The SPCE consists of 43 items with 9 domains of human emotions: Happiness, Anger, Fear, Sadness, Disgust, Shame, Guilt, Alpha pride, and Beta pride. The items are scored from 0 (‘did not feel at all’) to 6 (‘felt extremely strongly’). The 9 domains can be addressed in 2 groups of positive (Happiness, Alpha pride, and Beta pride) and negative (Anger, Fear, Sadness, Disgust, Shame, and Guilt) emotions (Tanke et al., 2024 ). Statistical analysis The statistical analysis used in this study was the same as that in Study 1. As approximately 25% of women had bonding disorder when bonding was measured using the SPCE (Hada, Ohashi et al., 2024 ), we set the base rate of the putative taxon as 0.25 in the taxometric analysis for the SPCE. Results Indicator selection As the SPCE has nine (i.e., 3 or more) subscales, these subscale scores could be candidate indicators for the taxometric analysis. The mean, SD, skewness, and kurtosis of all candidates used as input indicators for the taxometric analysis of the SPCE were calculated (Table 9). The skewness and kurtosis of the SPCE subscale scores were normally distributed. INSERT TABLE 9 AROUND HERE We then checked Cohen’s d between and within-group correlations for the putative taxa and complements of each set of 3 indicators. The candidates for the input indicators of the SPCE were 9 subscales separated into positive and negative groups. Because emotions have positive and negative valences, the SPCE subscales can be separately used with positive (i.e., Happiness, Alpha pride, and Beta pride) and negative (i.e., Anger, Fear, Sadness, Disgust, Shame and Guilt) emotion domains (Hada & Kitamura, 2024 ; Hada, Usui et al., 2024 ; Kitamura, Hada, et al., 2024 ; Tanke et al., 2024 ). Therefore, we checked Cohen’s d between and within-group correlations for the putative taxa and complements of Happiness, Alpha pride, and Beta pride, which belonged to the positive emotion domain of the SPCE (Table 10). Among the subscales of the negative emotion domain of the SPCE, Cohen’s d between and within-group correlations for the putative taxa and complements for 20 combinations ( 6 C 3 : select 3 indicators from 6 candidate indicators) were checked to determine the appropriate combination as input indicators for the taxometric analysis (Supplemental Table). We selected the combination with a Cohen’s d ≥ 1.25 and the mean within-group correlations for the putative taxon and complement < 0.3 (Table 11). Finally, we identified Happiness, Beta pride, and Alpha pride for the positive emotion domain of the SPCE, followed by Combination 1 (Anger, Fear, and Sadness), Combination 6 (Shame, Anger, and Fear), Combination 10 (Fear, Disgust, and Shame), Combination 16 (Disgust, Anger, and Fear), Combination 17 (Shame, Anger, and Fear), and Combination 20 (Fear, Disgust, and Guilt) for the negative emotion domain of the SPCE. INSERT TABLES 10 AND 11 AROUND HERE MAXEIG, MAMBAC, and L-Mode analyses The MAMBAC, MAXEIG, L-mode, and mean CCFI values for each scale are listed in Table 12. The mean CCFI for the positive emotion domain of the SPCE was 0.414 (Fig. 2). The curves with the results for the positive emotion domains (a dark line) are shown in graphics layered above the results for the categorical and dimensional comparison data. In the graphics, the plots of the middle 50% of the data points are shown as grey bands, and the minimum and maximum values are shown as thin lines. The curves for the positive emotion domains were much closer to those for the dimensional rather than categorical data. Because the positive emotion domains were likely to appear dimensional, the CCFI profiles were omitted from the next step. For the negative emotion domains, the mean CCFIs of Combinations 1 (Anger, Fear, and Sadness), Combination 6 (Guilt, Anger, and Fear), Combination 10 (Fear, Disgust, and Shame), Combination 16 (Disgust, Anger, and Fear), Combination 17 (Shame, Anger, and Fear), and Combination 20 (Fear, Disgust, and Sadness) were 0.645, 0.583, 0.565, 0.605, 0.557, and 0.513, respectively. All mean CCFIs, except for the negative emotion domains, exceeded 0.45, indicating ambiguous or categorical data. INSERT TABLE 12 AND FIG. 2 AROUND HERE Generating CCFI profiles The negative emotion domains appeared ambiguous or categorical rather than dimensional, and we generated the CCFI profiles (Table 13). The CCFI profiles of MAMBAC, MAXEIG, L-Mode, and mean profile values for Combination 1 (Anger, Fear, and Sadness) were 0.499, 0.654, 0.523, and 0.558, respectively. The combination with the next highest mean CCFI was Combination 17 (Shame, Anger, and Fear), of which the MAMBAC, MAXIEG, L-Mode, and Mean CCFI profile values were 0.583, 0.508, 0.511, and 0.533, respectively. The combination with the third-highest mean CCFI profile was Combination 16 (Disgust, Anger, and Fear), of which the MAMBAC, MAXIEG, L-Mode, and mean CCFI profile values were 0.489, 0.576, 0.488, and 0.517, respectively. The mean CCFI profiles for Combinations 1, 17, and 16 exceeded 0.500. Regarding the mean CCFI profiles for each combination, only Combination 1 for the negative emotion domains exceeded 0.550, indicating categorical data. In terms of the base rate estimation, the RunCCFIProfile() function provided a mean profile estimate of 0.276 for Combination 1 (Fig. 3), 0.266 for Combination 17, and 0.263 for Combination 16. These curves indicated the categorical nature of the data. INSERT TABLE 13 AND FIG. 3 AROUND HERE Discussion Study 3 showed that SPCE positive emotion domains (i.e., Happiness, Alpha pride, and Beta pride) indicated the dimensionality of the data. Psychological phenomena such as mothers’ lack of or excessive intensity of positive emotions towards their child may not be a clinical entity but a matter of degrees. However, the SPCE negative emotion domains, particularly the combination of Anger, Fear and Sadness of the bonding disorder measured by the SPCE, indicated the categorical nature of the data, marked by the highest mean CCFI profile. In addition, the CCFIs for the two combinations of Disgust, Anger, and Fear and Shame, Anger, and Fear were shown to be above 0.500 and were categorical rather than dimensional. Combinations of Anger and Fear may result in pathological symptoms of bonding disorder. Therefore, bonding disorder may deserve to be acknowledged as a clinical entity. GENERAL DISCUSSION To the best of our knowledge, this study is the first taxometric analysis of maternal bonding. Our findings showed that the CCFI profiles for the combination of Anger, Fear and Sadness of parent-to-child emotions measured by the SPCE suggested the presence of categorical data (i.e., mean CCFI profiles of those above 0.550). Mothers’ anger, fear, and sadness towards their child are likely to have a latent construct (i.e., a taxon). Hence, we believe that these three maternal emotions towards the child (as a taxon) are core symptoms of mother-to-child bonding disorders. It is of note that the CCFI profiles for the combinations of Shame, Anger and Fear, and that of Disgust, Anger, and Fear showed ambiguous data. Thus, the two indicators (Anger and Fear) may be singled out as core symptoms of bonding disorder. Despite numerous psychological symptoms and personality types being dimensional or ambiguous (Haslam, 2019 ; Haslam et al., 2020 ), our data on the SPCE’s negative domains suggested categorical nature of the concept. In contrast to the above results, MIBS, PBQ, and SPCE positive domains suggested the dimensionality of the data. Psychological symptoms measured using these indicators were likely to capture bonding difficulties without the presence of discrete pathological entities. Ruscio et al. ( 2006 ) pointed out that a dimensional construct may comprise several dimensions, some of which may subsume or be subsumed by dimensions at higher or lower levels within the nested broader construct (p. 16). Further exploration and taxon seeking are required. The CCFI profiles for the combination of Anger, Fear and Sadness of parent-to-child emotions measured by the SPCE provided a base rate estimation of 0.276 (i.e., taxon size = 27.6%). Surprisingly, the value of the base rate estimation was in accordance with the findings of the typology of parental bonding measured by the SPCE (Hada et al., 2024 ). In our previous study, the cases of mothers classified into the bonding disorder cluster were 29.0% ( n = 656/2,264). Bonding disorder cluster cases have shown significantly higher Anger, Fear and Sadness scores than other clusters (Hada et al., 2024 ). In this line, core symptoms of bonding disorder are likely to be Anger, Fear and Sadness emotions. Furthermore, additional evidence adds to the literature. The Dimensional Assessment of Mother Baby Organisation Questionnaire-11 (DAMBO-Q11) includes Anger-, Fear-, and Guilt-items elicited from the DAMBO-Q33 (T. and F. Kitamura Foundation for Studies and Skill Advancement in Mental Health). The Anger-, Fear-, and Guilt-items of the DAMBO-Q11 were selected based on the findings showing a high Area Under Curve (AUC) detected antenatal psychological symptoms (APS), including the foetal bonding disorder. Each item from the 9 domains targeting the foetal bonding disorder from the SPCE was adopted into items of the DAMBO-Q33. These items were selected based on the amount of information and item characteristics in terms of the item response theory (IRT) (Kitamura, Yamamoto et al., 2025 ). Thus, regardless of whether different methods or populations were used, Anger and Fear were elicited as the core items for bonding disorders. Parts of bonding disorders reflected by the MIBS or PBQ overlap with those in the SPCE. However, the SPCE was created by focusing on the concepts of human emotion (Hada et al., 2024 ). Basic emotions have automatic appraisal mechanisms that are not only quick but also occur without awareness (Ekman, 1977 ; Lazarus, 1991 ) and motivate human behaviour. If parental bonding is defined as emotions such as emotional ties or affective bonds, cognitive or behavioural aspects should be distinguished from them. Kinsey and Hupcey ( 2013 ) noted the following: Behavioral and biological indicators may promote maternal–infant bonding or be an outcome of maternal–infant bonding, but are not sufficient to determine the quality of maternal–infant bonding nor are these indicators unique to the concept. Thus, the SPCE is likely to detect categorical and pathological cases in terms of emotional bonding more accurately than the MIBS or PBQ, which include cognitive and behavioural aspects in addition to emotional items. Our study had several limitations. First, three samples for the taxometric analysis were obtained from different study samples. Although we examined the CCFI profiles, which were less biased and more accurate, the sample with simultaneous assessment by the MIBS, PBQ, and SPCE was ideal. Second, we used patient-reported outcome measures. Interviews or observational data, including structured interviews or laboratory assessments, should be used to assess parent–child bonding. Third, our study focused only on mother-to-child emotions. Fathers also form affectionate bonds with their children; therefore, further studies simultaneously examining both parents may shed light on the symptomatic structures of parental bonding. Despite these limitations, our findings may contribute to the definition and conceptualisation of (maternal) bonding disorders. CONCLUSIONS We expect the SPCE to become an attractive and promising assessment tool for measuring parental bonding in future research and practice. These efforts would contribute to identifying cases of bonding disorders that require care, thereby promoting parent-to-child bonding. Declarations Ethics Statement This study was conducted in accordance with the guidelines of the Declaration of Helsinki and was approved by the Research Ethics Committee of the Kitamura Institute of Mental Health Tokyo for Samples 1 and 2 (no. 2020030501, 21 March 2020) and Sample 3 (no. 2021101401, 13 November 2021). All participants were informed about the aims of the study, ethical considerations for participation, security of personal information, and affiliation of the principal investigator. Anonymity and voluntary participation were assured. Appropriate informed consent, including an electronic informed consent form, was obtained from all participants involved in this study. Funding This study was supported by the JSPS KAKENHI (grant number 21H03255; PI: Yukiko Ohashi). Conflict of Interest The authors declare no conflict of interest. Author Contributions Ayako Hada: Conceptualisation, Formal analysis, Methodology, Writing – original draft. Toshinori Kitamura: Conceptualisation, Methodology, Project administration, Supervision, Writing – review and editing. All the authors have read and agreed to the published version of the manuscript. Data Availability The dataset analysed and used in this study is available upon reasonable request from the first author. References Baba K, Kataoka Y, Kitamura T (2023) Identifying core items of the Japanese version of the Mother-to-Infant Bonding Scale for diagnosing postpartum bonding disorder. Healthcare 11:1740. https://doi.org/10.3390/healthcare11121740 Baba K, Takauma F, Tada K, Tanaka T, Sakanashi K, Kataoka Y, Ktamura T (2017) Factor structure of the Conflict Tactics Scale. Int J Community Based Nurs Midwifery 5:239–247 Branjerdporn G, Meredith P, Strong J, Garcia J (2017) Associations between maternal-foetal attachment and infant developmental outcomes: A systematic review. Matern Child Health J 21(3):540–553. https://doi.org/10.1007/s10995-016-2138-2 Brockington IF, Aucamp HM, Fraser C (2006) Severe disorders of the mother-infant relationship: definitions and frequency. Arch Women Ment Health 9:243–251 Brockington IF, Oates J, George S, Turner D, Vostanis P, Sullivan M, Murdoch C (2001) A screening questionnaire for mother-infant bonding disorders. Arch Women Ment Health 3:133–140 Ekman P (1977) Biological and cultural contributions to body and facial movement. In: Blacking J (ed) Anthropology of the body. Academic, pp 39–84 Faisal-Cury A, Tabb KM, Ziebold C, Matijasevich A (2021) The impact of postpartum depression and bonding impairment on child development at 12 to 15 months after delivery. J Affect Disorders Rep 4:100125 Hada A, Kitamura T (2024) Factor structures and clusters of psychological symptoms during pregnancy: Proposal of antenatal psychological syndrome. In: Kitamura T (ed) Dimensional Assessment of Mother Baby Organization Project: Many facets of psychological difficulties among expectant women. Nova Publishing, pp 117–144 Hada A, Kubota C, Imura M, Takauma F, Tada K, Kitamura T (2019) The Edinburgh Postnatal Depression Scale: Model comparison of factor structure and its psychosocial correlates among mothers at one month after childbirth in Japan. Open Family Stud J 11:1–17. https://doi.org/10.2174/1874922401911010001 Hada A, Ohashi Y, Usui Y, Kitamura T (2024) A scale of parent-to‐child emotions: adaptation, factor structure, and measurement invariance. Fam Process 63(3):1677–1701. https://doi.org/10.1111/famp.12919 Hada A, Ohashi Y, Usui Y, Kitamura T (2024) Typology of parent-to-child emotions: A study of Japanese parents of a foetus up to a 12-year-old child. Healthcare 12(9):881. https://doi.org/10.3390/healthcare12090881 Hada A, Usui Y, Ohashi Y, Takeda S, Kitamura T (2024) Typology of pregnant women’s bonding emotions towards their foetus: A study of Japanese women in the first trimester. Psychology 15(3):329–340 Haslam N (2019) Unicorns, snarks, and personality types: A review of the first 102 taxometric studies of personality. Australian J Psychol 71(1):39–49 Haslam N, McGrath MJ, Viechtbauer W, Kuppens P (2020) Dimensions over categories: A meta-analysis of taxometric research. Psychol Med 50(9):1418–1432 Hashijiri K, Watanabe Y, Fukui N, Motegi T, Ogawa M, Egawa J, Someya T (2021) Identification of bonding difficulties in the peripartum period using the Mother-to-Infant Bonding Scale-Japanese Version and its tentative cutoff points. Neuropsychiatr Dis Treat 17:3407–3413. https://doi.org/10.2147/NDT.S336819 Kendell RE, Brockington IF (1980) The identification of disease entities and the relationship between schizophrenic and affective psychoses. Br J Psychiatry 137(4):324–331 Kinsey C, Hupcey JE (2013) State of the science of maternal-infant bonding: A principle-based concept analysis. Midwifery 29(12):1314–1320. https://doi.org/10.1016/j.midw.2012.12.019 Kitamura T, Hada A, Usui Y, Ohashi Y (2025) Clusters and case vignettes of maternal-foetal bonding disorders: A mixed methods approach. (under review; submited to Psychiatry and Clinical Neurosciences Reports ) Kitamura T, Takauma F, Tada K, Yoshida K, Nakano H (2004) Postnatal depression, social support, and child abuse. World Psychiatry 3:100–101 Kitamura T, Takegata M, Haruna M, Yoshida K, Yamashita H, Murakami M, Goto Y (2015) The Mother-Infant Bonding Scale: Factor structure and psychosocial correlates of parental bonding disorders in Japan. J Child Fam stud 24:393–401. https://doi.org/10.1007/s10826-013-9849-4 Kitamura T, Yamamoto M, Saito T, Hada A, Tanke A, Usui Y, Ishida H (2025) Development and validation of a multidimensional mental health screening questionnaire for pregnant women: A preliminary report. Psychiatry Clin Neurosciences Rep 4(1). https://doi.org/10.1002/pcn5.70053 Kumar RC (1997) Anybody's child: Severe disorders of mother-to-infant bonding. Br J Psychiatry 171(2):175–181 Lazarus RS (1991) Emotion and adaptation. Oxford University Press Le Bas GA, Youssef GJ, Macdonald JA, Rossen L, Teague SJ, Kothe EJ, McIntosh JE, Olsson CA, Hutchinson DM (2018) The role of antenatal and postnatal maternal bonding in infant development: A systematic review and meta-analysis. Soc Dev 29:3–20 Matsunaga A, Ohashi Y, Sakanashi K, Kitamura T (2021) Factor structure of the Postpartum Bonding Questionnaire: Configural invariance and measurement invariance across postpartum time periods. J Psychiatr Res 135:1–7 Matsunaga A, Takauma F, Tada K, Kitamura T (2017) Discrete category of mother-to-infant bonding disorder and its identification by the Mother-to-Infant Bonding Scale: A study in Japanese mothers of a 1-month-old. Early Hum Dev 111:1–5 Meehl PE (1965) Detecting latent clinical taxa by fallible quantitative indicators lacking an accepted criterion. Retrieved from the University Digital Conservancy. https://hdl.handle.net/11299/151479 Meehl PE (1992) Factors and taxa, traits and types, differences of degree and differences in kind. J Pers 60(1):117–174. https://doi.org/10.1111/j.1467-6494.1992.tb00269.x Meehl PE, Yonce LJ (1994) Taxometric analysis: I. Detecting taxonicity with two quantitative indicators using means above and below a sliding cut (MAMBAC procedure). Psychol Rep 74(3):1059–1274 Meehl PE (1995) Bootstraps taxometrics: Solving the classification problem in psychopathology. Am Psychol 50(4):266–275 Meehl PE, Yonce LJ (1996) Taxometric analysis: II. Detecting taxonicity using covariance of two quantitative indicators in successive intervals of a third indicator (Maxcov procedure). Psychol Rep 78(3, Pt 2):1091–1227 Muzik M, Bocknek EL, Broderick A, Richardson P, Rosenblum KL, Thelen K, Seng JS (2013) Mother–infant bonding impairment across the first 6 months postpartum: The primacy of psychopathology in women with childhood abuse and neglect histories. Arch Women Ment Health 16(1):29–38. https://doi.org/10.1007/s00737-012-0312-0 Ohashi Y, Takegata M, Haruna M, Kitamura T, Takauma F, Tada K (2015) Association of specific negative life events with depression severity one month after childbirth in community-dwelling mothers. Int J Nurs Health Sci 2:13–20 Ohashi Y, Kitamura T, Sakanashi K, Tanaka T (2016) Postpartum bonding disorder: factor structure, validity, reliability and a model comparison of the postnatal bonding questionnaire in Japanese mothers of infants. Healthcare 4(3):50 Ohashi Y, Sakanashi K, Tanaka T, Kitamura T (2016) Mother-to-infant bonding disorder, but not depression, 5 days after delivery is a risk factor for neonate emotional abuse: A study in Japanese mothers of 1-month olds. Open Family Stud J 8:27–36 Røhder K, Væver MS, Aarestrup AK, Jacobsen RK, Smith-Nielsen J, Schiøtz ML (2020) Maternal-fetal bonding among pregnant women at psychosocial risk: The roles of adult attachment style, prenatal parental reflective functioning, and depressive symptoms. PLoS ONE, 15(9), e0239208 Ruscio J, Carney LM, Dever L, Pliskin M, Wang SB (2018) Using the Comparison Curve Fit Index (CCFI) in taxometric analyses: Averaging curves, standard errors, and CCFI profiles. Psychol Assess 30(6):744–754 Ruscio J, Kaczetow W (2009) Differentiating categories and dimensions: Evaluating the robustness of taxometric analyses. Multivar Behav Res 44:259–280 Ruscio J, Ruscio AM, Haslam N (2006) Introduction to the taxometric method: A practical guide (1st ed.). Routledge. https://doi.org/10.4324/9780203726549 Ruscio J, Ruscio AM, Meron M (2007) Applying the bootstrap to taxometric analysis: Generating empirical sampling distributions to help interpret results. Multivar Behav Res 42:349–386 Ruscio J, Walters GD, Marcus DK, Kaczetow W (2010) Comparing the relative fit of categorical and dimensional latent variable models using consistency tests. Psychol Assess 22(1):5–21 Ruscio J, Ruscio AM, Carney LM (2011) Performing taxometric analysis to distinguish categorical and dimensional variables. J Experimental Psychopathol 2(2):170196 Ruscio J, Wang SB (2021) RTaxometrics: Taxometric Analysis. R package version 3.2. https://cran.r-project.org/package=RTaxometrics Ruscio J, Wang SB (2022) Taxometric analysis. In G. J. G. Asmundson (Ed.), Comprehensive Clinical Psychology. (2nd Ed.), Vol. 3 , pp. 148–175. New York: Elsevier Sims A (1988) Symptoms in the mind. Paris, France, Baillière Tindall Straus MA, Hamby SL, Finkelhor D, Moore DW, Runyan D (1998) Identification of child maltreatment with the Parent-Child Conflict Tactics Scales: Development and psychometric data for a national sample of American parents. Child Abuse Negl 22(4):249–270. https://doi.org/10.1016/S0145-2134(97)00174-9 T. and F. Kitamura Foundation for Studies and Skill Advancement in Mental Health. (2022) 33-item Dimensional Assessment of Mother Baby Organization Questionnaire . Author, Tokyo. (available as an e-book) Trombetta T, Giordano M, Santoniccolo F, Vismara L, Della Vedova AM, Rollè L (2021) Pre-natal attachment and parent-to-infant attachment: A systematic review. Front Psychol 12. https://doi.org/10.3389/fpsyg.2021.620942 Tanke A, Hada A, Kitamra T (2024) Maternal-foetal bonding disorder: Factor structure and correlates. In: Kitamura T (ed) Dimensional Assessment of Mother Baby Organization Project: Many facets of psychological difficulties among expectant women. Nova Publishing, pp 49–66 Yamamoto M, Takauma F, Tada K, Baba K, Kitamura T (2023) Factor Structure and Measurement Invariance of the Japanese Version of the Mother-to-Infant Bonding Scale. Adv Reproductive Sci 11(4):159–170 Yoshida K, Yamashita H, Conroy S, Marks M, Kumar C (2012) A Japanese version of Mother-to-Infant Bonding Scale: Factor structure, longitudinal changes and links with maternal mood during the early postnatal period in Japanese mothers. Arch Women Ment Health 15(5):343–352. https://doi.org/10.1007/s00737-012-0291-1 Waller NG, Meehl PE (1998) Multivariate taxometric procedures: Distinguishing types from continua. Sage Publications, Inc. Tables Table 1 to 13 are available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files Table113.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6858320","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":468917479,"identity":"548348ed-addc-444f-a3d9-ced1fe74b7bb","order_by":0,"name":"Ayako Hada","email":"","orcid":"https://orcid.org/0000-0002-2835-8456","institution":"Kitamura Institute of Mental Health Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Ayako","middleName":"","lastName":"Hada","suffix":""},{"id":468917480,"identity":"bf73ce0c-b1d4-4043-a5bc-c0b0424f0874","order_by":1,"name":"Yukiko Ohashi","email":"","orcid":"https://orcid.org/0000-0003-3707-0742","institution":"Josai International University","correspondingAuthor":false,"prefix":"","firstName":"Yukiko","middleName":"","lastName":"Ohashi","suffix":""},{"id":468917481,"identity":"a26783f3-be3f-4f66-bb8b-69aadff001e5","order_by":2,"name":"Yuriko Usui","email":"","orcid":"","institution":"University of Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Yuriko","middleName":"","lastName":"Usui","suffix":""},{"id":468917482,"identity":"74b5db02-1dab-4fb6-8cfb-5eb20133aad3","order_by":3,"name":"Kyoko Sakanashi","email":"","orcid":"","institution":"Kumamoto Midwives Association","correspondingAuthor":false,"prefix":"","firstName":"Kyoko","middleName":"","lastName":"Sakanashi","suffix":""},{"id":468917483,"identity":"c023b478-11f9-4020-922d-5c5c2b5aac31","order_by":4,"name":"Tomoko Tanaka","email":"","orcid":"","institution":"Yamaga Health Centre","correspondingAuthor":false,"prefix":"","firstName":"Tomoko","middleName":"","lastName":"Tanaka","suffix":""},{"id":468917484,"identity":"5b60c8b8-545d-4d55-8361-721713306bd7","order_by":5,"name":"Fumie Takauma","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Fumie","middleName":"","lastName":"Takauma","suffix":""},{"id":468917485,"identity":"2a5123c2-f129-456d-bad7-e83dd128b735","order_by":6,"name":"Katsuhiko Tada","email":"","orcid":"","institution":"Okayama Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Katsuhiko","middleName":"","lastName":"Tada","suffix":""},{"id":468917486,"identity":"c13357c2-93b4-4293-889e-a0fb5f884b5a","order_by":7,"name":"Toshinori Kitamura","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-2326-3140","institution":"Kitamura Institute of Mental Health Tokyo","correspondingAuthor":true,"prefix":"","firstName":"Toshinori","middleName":"","lastName":"Kitamura","suffix":""}],"badges":[],"createdAt":"2025-06-10 02:43:13","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6858320/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6858320/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84735456,"identity":"0fd98b3e-d51e-45dc-b372-10014f3691e5","added_by":"auto","created_at":"2025-06-16 18:19:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":36352,"visible":true,"origin":"","legend":"\u003cp\u003eProcedure of indicator selection for the taxometric analysis\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6858320/v1/2181c6f825224bd685d6fe39.png"},{"id":84736440,"identity":"b392ac43-fa9c-4f42-bad7-daabdd9167c8","added_by":"auto","created_at":"2025-06-16 18:35:29","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":157380,"visible":true,"origin":"","legend":"\u003cp\u003eCCFIs for the SPCE positive domains\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6858320/v1/2e5e34c3bf7effb9adaa9bb7.jpeg"},{"id":84735460,"identity":"6fe0f9fb-b9f7-49b5-9a87-127e4a500520","added_by":"auto","created_at":"2025-06-16 18:19:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":11128,"visible":true,"origin":"","legend":"\u003cp\u003eCCFIs for Combination 1 (Anger, Fear, and Sadness) of the SPCE negative domains\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6858320/v1/1f94e7ecec52b284cbc71345.png"},{"id":84736792,"identity":"3cee082d-f70a-456c-9d37-dc4af779f6d0","added_by":"auto","created_at":"2025-06-16 18:43:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1090461,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6858320/v1/498a9751-5cba-4200-8bf8-f4701a52276a.pdf"},{"id":84735455,"identity":"4938f089-4023-4a07-b973-81119986b44c","added_by":"auto","created_at":"2025-06-16 18:19:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":41379,"visible":true,"origin":"","legend":"","description":"","filename":"Table113.docx","url":"https://assets-eu.researchsquare.com/files/rs-6858320/v1/874ab26412ecab2f44be9edf.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eSearching the core symptoms of perinatal bonding disorders: A series of taxometric analyses for three bonding scales\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eDetermining whether sets of psychological symptoms are categorical or dimensional has a significant impact on the research, theory, and practice of psychiatry and psychology. Kendel and Brockington (1980) noted;\u003c/p\u003e \u003cp\u003eIf the term entity is to have any meaning at all in contemporary psychiatry, it should imply the existence of a natural boundary or discontinuity between the condition in question and its neighbours. To use the imagery of the old aphorism that classification is art of carving nature at the joints, it should imply that the there is indeed a joint there, that we are not sawing through bone. The most obvious way of doing this is to demonstrate, in a representative and unselected population, that patients exhibiting a mixture of the symptoms of the condition in question and those of neighbouring syndromes are relatively uncommon. The mixed forms, the greys, must be shown to be less common than the pure forms, the blacks and the whites, which in mathematical terms involves demonstrating that a distribution of scores on a linear variable, derived from the relevant symptoms, is bimodal with a \u0026lsquo;point of rarity\u0026rsquo; in the middle, rather than unimodal.\u003c/p\u003e \u003cp\u003eOnce a categorical structure is identified for them, those symptoms are presumed to be qualitatively different from others. The cases belonging to a single categorical group indicating a pathology should be identified. However, if a categorical structure is refuted, these psychological symptoms are seen as continuous, differing only in the degree of severity and \u003cem\u003eappearing\u003c/em\u003e to be pathological when the symptoms are severe.\u003c/p\u003e \u003cp\u003eA drawback of the issue of \u0026lsquo;point of rarity\u0026rsquo; is that a pathological entity usually comprises multiple rather than a single symptom (indicator). Thus, although the distribution of the severity of a symptom seems continuous (without the point of rarity), this may not be the case when looking at the multidimensional space using more than one symptom simultaneously. Therefore, a multivariate distribution should be considered. This is called as taxometrics technique (Meehl, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1965\u003c/span\u003e \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Ruscio et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Waller \u0026amp; Meehl, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). A group that can be clearly distinguished from others is called a taxon, and taxa can be generated using a step function applied to quantitative variables (Waller \u0026amp; Meehl, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Ruscio et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHistorically, an impaired or disordered emotional bond of parent to child has been viewed as pathological: bonding disorder (Brockington et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Kitamura et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kumar, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Severe bonding disorder leads to negative effects on the relationship between a parent and child dyad. For example, postnatal women with higher scores in bonding disorder showed deficiencies in positive parenting behaviours such as low affective sensitivity, low warmth, and low engagement and flexibility (Muzik et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Such relationships negatively affect children\u0026rsquo;s development. Bonding impairment at 6\u0026ndash;8 months after childbirth was associated with child developmental delay at 12\u0026ndash;15 months postnatally (Faisal-Cury et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, a study using a cluster analysis of maternal bonding after childbirth (Matsunaga et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) reported that a cluster with a high score of the Japanese version of the Mother-to-Infant Bonding Scale (MIBS-J; Yoshida et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) indicating the risk of bonding disorders showed high scores of the psychological abuse subscale of the Parent\u0026ndash;Child Conflict Tactics Scale (CTSPC: Straus et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Postnatal bonding disorder, but not depression, was a predictor of neonatal abuse (Ohashi, Sakanashi et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). On the other hand, high quality of maternal bonding contributes to better infant development outcomes (Le Bas et al., 2019).\u003c/p\u003e \u003cp\u003eNevertheless, the symptomatology and nosology of bonding disorders remain unclear. Despite an overwhelming amount of research on the causes, risk factors, and consequences of bonding disorders (e.g., Branjerdporn et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; R\u0026oslash;hder et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Trombetta et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), few studies have contributed to the concept and nosology of the parental bonding disorder. What symptoms should be included under the rubric of bonding disorder? No consensus exists on the diagnostic criteria or the definition of bonding disorder based on symptomatology or nosology. Bonding disorder researchers included different types of symptoms under the rubric of bonding disorder. They include emotional (e.g., \u0026lsquo;I am fond of my baby\u0026rsquo;, \u0026lsquo;I hate my baby\u0026rsquo;), motivational (e.g., \u0026lsquo;I want to protect my baby\u0026rsquo;), and behavioural (e.g., \u0026lsquo;I feed my baby with joy\u0026rsquo;, \u0026lsquo;I hit my baby\u0026rsquo;) symptoms (indicators). Identifying the core symptoms of bonding disorders is important in research and clinical settings. Many instruments have been used to measure parental bonding and bonding disorders, and almost all of these measures have a multiple factorial structure. The MIBS-J had a 2-factor structure (Kitamura et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yoshida et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The Postnatal Bonding Questionnaire (PBQ; Brockington et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) had a 3-factor structure (Matsunaga et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ohashi, Kitamura et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The Scale of Parent-to-Child Emotions (SPCE; Hada et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which is a new scale for measuring parents\u0026rsquo; bonding emotions, has 9 domains. Thus, the symptoms of bonding disorder can be explained by combining several symptoms.\u003c/p\u003e \u003cp\u003eNosology begins with the dissection, examination, and identification of abnormalities in organs and tissues visible to the naked eye. It distinguishes between unhealthy and healthy tissues. The same applies to invisible psychological symptoms. The identification of pathological symptoms is likely to be useful for classification. This involves two important issues: (a) which symptoms should be included in bonding disorder and (b) what are the core symptoms of bonding disorders? These are interdependent. Whether we can identify a taxon largely depends on which symptoms (indicators) are included under the rubric of the entity in question. A categorical taxon can be identified only when we include \u0026lsquo;core\u0026rsquo; symptoms (indicators) under such an entity to be entered into a taxometric analysis. In other words, \u0026lsquo;core\u0026rsquo; symptoms mean those that can identify a taxon. Demonstrating the dimensionality of an entity (using a specific number of indicators) may not necessarily be a proof of \u003cem\u003elack\u003c/em\u003e of taxonicity because a different combination of symptoms (indicators) may prove the existence of a taxon. Thus, dimensionality is a \u0026lsquo;null hypothesis\u0026rsquo; in taxometrics. The null hypothesis is proven only when repeated trials (using different combinations of symptoms) fail to prove the alternative hypothesis. Furthermore, the proof of taxonicity is also categorical and \u003cem\u003enot\u003c/em\u003e a matter of degree.\u003c/p\u003e \u003cp\u003eIn this study, we used three sets of data in which different bonding disorder measures\u0026mdash;MIBS-J, PBQ, and SPCE\u0026mdash;were used among Japanese postnatal women to explore the taxon of the pathology of parent-to-child bonding, which has core bonding disorder symptoms. In the third part using the SPCE data, we compared different combinations of parent-to-child \u003cem\u003eemotions\u003c/em\u003e so that we could identify the combination that showed the greatest degree of taxonicity.\u003c/p\u003e"},{"header":"RESEARCH DESIGN","content":"\u003cp\u003eThis exploratory research used a taxometric analysis consisted of three parts. Three cross-sectional data samples from previous studies were analysed in the same statistical manner. The details of these samples are described in each subsequent section.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy 1: MIBS-J\u003c/h2\u003e \u003cp\u003eThe MIBS-J is well known and is the most widely used tool for detecting postpartum bonding disorder in clinical settings in Japan. The MIBS was originally based on the Mother Infant Bonding Questionnaire (MIBQ: Kumar, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), which was selected from the mother\u0026rsquo;s narrative accounts. Both MIBQ and MIBS were developed to screen the general population for problems in the mother\u0026rsquo;s feelings towards her new baby. Several studies have validated the MIBS-J for clinical use (Hashijiri et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kitamura et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Matsunaga et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yoshida et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe data were obtained from a questionnaire survey on postpartum depression at two time points (Time 1 at 5 days and Time 2 at 1 month after childbirth) (Baba et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hada et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Matsunaga et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ohashi, Takegata et al., 2016). The survey was conducted between August 2001 and April 2002. Women who gave birth were recruited at five obstetric clinics in Okayama, Japan. Women who were not fluent in Japanese were excluded. Approximately 1,530 women were eligible for the study, of whom 1,200 (78%) received the questionnaires and 758 (63%) of those returned the questionnaires at both time points. Participants with missing values in the MIBS were excluded, and 723 (95%) participants were included in the sample. The participants\u0026rsquo; mean (standard deviation [SD]) age was 28.7 (4.1) years; 444 (58.6%) participants already had already a child. We used the sample of mothers who had given birth a month after childbirth.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurements\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eJapanese version of Mother-to-Infant Bonding Scale (MIBS-J; Yoshida 2012)\u003c/h2\u003e \u003cp\u003eThe MIBS-J is a scale consisting of 10 items rated a 4-point Likert scale (0 = \u0026lsquo;not at all\u0026rsquo; to 3 = \u0026lsquo;very much\u0026rsquo;). The items reflect the mother\u0026rsquo;s feelings towards her infant. Higher scores indicate worse mother-to-infant bonding. A factor analysis of the MIBS-J was reported indicating a 2-factor structure: Anger and Rejection (AR) and Lack of Affect (LA) (Kitamura et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yoshida et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Furthermore, a three-item structure consisting of Item 1 (\u0026lsquo;I feel loving towards my child\u0026rsquo;; loving), Item 6 (\u0026lsquo;I enjoy doing things with my child\u0026rsquo;; enjoying), and Item 8 (\u0026lsquo;I feel protective towards my child\u0026rsquo;; protective) was accepted for strict invariance across several time points after childbirth (Baba et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yamamoto et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eTaximetrics is a powerful analytical technique to determine whether a construct of interest is categorical or dimensional. The taxometric methodology is characterised by the use of several mathematically independent procedures. Taxometric methods include the mean above minus below a cut (MAMBAC; Meehl \u0026amp; Yonce, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), MAXimum COVariance (MAXCOV; Meehl \u0026amp; Yonce, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), MAXimum EIGen value (MAXEIG; Waller \u0026amp; Meehl, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), and latent mode (L-Mode; Waller \u0026amp; Meehl, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) analyses. The comparison curve fit index (CCFI) examines whether the empirical data are closer to taxonomic or dimensional comparison data, which is useful for specifying and evaluating different operationalisations of consistency testing. The values of the CCFI ranged from 0 (strongest support for the dimensional structure) to 1 (strongest support for the taxonic structure), with a value of 0.50 representing the most ambiguous result (Ruscio et. al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The CCFI profile is a summary index of the CCFIs employed in a series of taxometric analysis procedures (i.e., MAMBAC, MAXEIG, and L-Mode) (Ruscio et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIndicator selection\u003c/h3\u003e\n\u003cp\u003eA taxometric analysis is required to meet several recommendations: (a) a sample size of 300 or more (Meehl \u0026amp; Yonce, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), (b) three or more indicators for MAXEIG and L-Mode (Waller \u0026amp; Meehl, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), (c) four or more ordered categories in indicators, (d) each indicator discriminates between putative taxa and complement groups at \u003cem\u003ed\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;1.25, and (e) within-group correlations for putative taxa and complement between indicators do not exceed 0.30. We examined the mean, SD, skewness, and kurtosis of the candidates as input indicators for the taxometric analysis. We then examined whether the candidate variables were validated for taxometric analysis in terms of Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e and within-group correlations for the putative taxon and putative complement before conducting the taxometric analysis. We set the base rate of the putative taxon to the prevalence rate of bonding disorders measured by each measurement to check whether the candidate variables were suitable for taxometric analysis. This is because cases can be assigned to putative groups based on prior theories, diagnostic criteria, or conventionally applied thresholds (Ruscio et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ruscio \u0026amp; Wang, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe goal for item selection in the taxometric analysis was to select 3 indicators for each measurement. As the meanings of the contents and constructs of each measurement of bonding were subtly different, indicators were needed to reflect these differences as symptoms. Therefore, it was necessary to consider which symptoms are core. The procedure of indicator selection for the taxometric analysis is shown in Fig.\u0026nbsp;1. If a measurement had three subscales, we used the three subscale scores as input indicators. The composite variables can be candidate input indicators for taxometric analyses. One advantage of forming composite variables is that the resulting input indicator may comprise a larger range of variables, providing a more reliable rank ordering of the cases (Ruscio et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). If a measurement has more than three subscales, all combinations of three subscales selected from all subscales were examined in terms of Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e and within-group correlations. The combination of three subscales that meet the criteria of Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e and the mean within-group correlation not exceeding 0.30 were selected as input indicators for the taxometric analysis. While datasets should meet each of these five recommendations (above a to e), several simulation studies have shown that boundary values on some of these criteria or a failure to meet one or more criteria may be counterbalanced by the favourable characteristics of other criteria in the same datasets (Ruscio et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Therefore, we selected all combinations indicating a Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;1.25 and mean within correlations\u0026thinsp;\u0026lt;\u0026thinsp;0.30 among all combinations of each of the 3 subscales for the input indicator.\u003c/p\u003e\n\u003ch3\u003eINSERT FIG. 1 AROUND HERE\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMAXEIG, MAMBAC, and L-Mode analyses\u003c/h2\u003e \u003cp\u003eTaxometric analyses were performed. First, MAMBAC, MAXEIG, and L-Mode were performed using the \u003cem\u003eRunTaxometrics()\u003c/em\u003e function in the Rtaxometrics package. The number of cuts along the input variable (i.e., \u003cem\u003en.cut\u003c/em\u003e parameter) was set to 50 cuts, and the number of cases at each extreme along the input variable before making the first and last cuts (i.e., \u003cem\u003en.end\u003c/em\u003e parameter) was set to 25 for the MAMBAC analysis. The number of overlapping windows was set to 50, and the proportion of overlap between windows was set to 0.90 in the MAXEIG analysis. The search for the left mode beyond 0.001 and the right mode beyond 0.001 was included in the L-Mode settings. We set the base rate of the putative taxon to the prevalence rate of bonding disorders measured by each measurement to be the same as that in the indicator selection process. Because approximately 15% of women had bonding disorders after childbirth (Matsunaga et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), we set the base rate of the putative taxon as 0.15 in the taxometric analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGenerating CCFI profiles\u003c/h2\u003e \u003cp\u003eAfter performing MAMBAC, MAXEIG, and L-Mode, we examined the CCFI profiles using the \u003cem\u003eRunCCFIprofile()\u003c/em\u003e function. When the taxometric results appear categorical, one may wish to estimate the relative sizes of the two groups, called taxon and complement. The relative sizes of the two groups are shown by taxon-base rate estimates and can be calculated using the CCFI profiles (Ruscio et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Examining the CCFI profile, which is a plot of CCFIs by taxon base rates, provides more accurate base rate estimates when the data appear to be categorical and offers clearer results when the data structure is ambiguous (Ruscio \u0026amp; Wang, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). We performed a taxometric analysis with CCFI profiles when the mean CCFI exceeded 0.45 because the ambiguous CCFIs ranged from 0.45 to 0.55 (Ruscio et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ruscio \u0026amp; Wang, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The Rtaxometrics package (Ruscio and Wang, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) was used for all taxometric analyses.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResults\u003c/h3\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIndicator selection\u003c/h2\u003e \u003cp\u003eFirst, we determined which subscales or items should be used as input indicators for the taxometric analysis. The MIBS-J was confirmed in only two subscales using Japanese samples (Yoshida et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kitamura et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Therefore, candidates for the input indicators of the MIBS-J for taxometric analysis were selected from its items. Because the MIBS-J has three stable (invariant) items (Items 1, 6, and 8) (Baba et al, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yamamoto et al, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), we considered these items as the input indicators for the taxometric analysis of the MIBS-J. Among the MIBS-J items, although Item 8 had a high kurtosis (6.82), the skewness and kurtosis of all other items indicated normal distributions (Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eINSERT TABLE 1 AROUND HERE\u003c/h2\u003e \u003cp\u003eSecond, we checked Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e between and within-group correlations for the putative taxa and complements of the 3 items (Items 1, 6, and 8) of the MIBS-J (Table\u0026nbsp;2). Within-group correlations for the putative taxa and complements for the MIBS-J were -0.29 and 0.12, respectively. All Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e were greater than 1.25. Finally, the 3 indicators (items 1, 6, and 8) were used for the taxometric analysis of the MIBS-J.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eINSERT TABLE 2 AROUND HERE\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003eMAXEIG, MAMBAC, and L-Mode analyses\u003c/h2\u003e \u003cp\u003eThe MAMBAC, MAXEIG, L-Mode, and mean CCFIs for the MIBS-J are shown in Table\u0026nbsp;3. The mean CCFI of the MIBS-J was 0.585.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eINSERT TABLE 3 AROUND HERE\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003eGenerating CCFI profiles\u003c/h2\u003e \u003cp\u003eBecause the MIBS-J data appeared ambiguous or categorical rather than dimensional, we generated the CCFI profiles. All CCFI profiles of the MIBS-J were below 0.500, and the mean CCFI profile was 0.427 (Table\u0026nbsp;4).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eINSERT TABLE 4 AROUND HERE\u003c/h2\u003e \u003c/div\u003e\n\u003ch3\u003eDiscussion\u003c/h3\u003e\n\u003cp\u003eThe findings of Study 1 showed that the three symptoms of bonding disorder (loving, enjoying, and protective) measured by the MIBS-J were likely to be dimensional. Even if a woman does not feel loving, enjoying, or protective towards her child, such symptoms are likely to vary in terms of degree. Although clinicians have paid attention to experiencing a delay in the onset or loss of maternal emotional responses, such as loving, enjoying, and protecting the infant (Brockington et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), these symptoms may not be the core of discrete bonding \u003cem\u003edisorder\u003c/em\u003e. The three indicators used in Study 1 (loving, enjoying, and protective) were invariant across measurement occasions (Baba et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yamamoto et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and did not endorse the core in terms of taxonicity. However, this finding does not necessarily refute the possibility that different combinations of symptoms (indicators) indicate the existence of a taxon.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eStudy 2: PBQ\u003c/h2\u003e \u003cp\u003eThe PBQ is also well-known as a scale for detecting bonding disorder worldwide as well as in Japan. The PBQ was validated in a study of mothers who were referred for emotional or psychological issues. However, because of its large number of items (25), the PBQ is unlikely to be preferred over the MIBS-J in clinical situations.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eMethods\u003c/h2\u003e \u003cdiv id=\"Sec24\" class=\"Section4\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe data was obtained from three time points across the phases of childbirth (Time 1 during pregnancy, Time 2 at 5 days after childbirth, and Time 3 at 1 month after childbirth). We recruited pregnant women of at least 28 weeks\u0026rsquo; gestation who attended antenatal clinics during the entire month of November 2011 (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,450). A set of questionnaires was distributed to these women during late pregnancy and at 5 days (while in the hospital) and 1 month (while attending the one-month health check-up) after childbirth. We focused on the symptoms of bonding disorder which were measured using the PBQ. For a taxometric analysis, a sample size of 300 or more is recommended (Meehl \u0026amp; Yonce, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Therefore, we used data from the Time 2 sample (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;418), excluding participants with missing values on the PBQ. These participants\u0026rsquo; mean (SD) age was 30.2 (4.7) years, and among them, 245 (58.6%) already had another child/chidren.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eMeasurement\u003c/h2\u003e \u003cdiv id=\"Sec26\" class=\"Section4\"\u003e \u003ch2\u003ePostpartum Bonding Questionnaire (PBQ; Brockington et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eThe PBQ is a scale consisting of 25 items reflecting a mother\u0026rsquo;s feelings (e.g., \u0026lsquo;I feel angry with my baby\u0026rsquo;), cognitions (e.g., \u0026lsquo;My baby cries too much\u0026rsquo;), attitudes (e.g., \u0026lsquo;I feel like hurting my baby\u0026rsquo;), and behaviour (e.g., \u0026lsquo;I have done harmful things to my baby\u0026rsquo;). These items are scored from 0 to 5. Higher scores indicate more negative feelings, cognitions, or attitudes towards the infant. The factor structure of the Japanese version of the PBQ has a 3-factor structure: Anger and Restrictedness (AR), Lack of Affection (LA), and Rejection and Fear (RF) (Matsunaga et al., 2020; Ohashi, Kitamura et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis used in this study was the same as that used in Study 1. We set the base rate of the putative taxon to the prevalence rate of bonding disorder measured by each measurement to be the same as in the indicator selection process. There were no data on the prevalence rate of bonding disorder, as measured by the PBQ, in Japan. Therefore, we set the base rate of the putative taxon as 0.15 in the taxometric analysis of the symptoms of bonding disorder for the PBQ, similar to the MIBS-J.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResults\u003c/h3\u003e\n\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eIndicator selection\u003c/h2\u003e \u003cp\u003eAs the PBQ has 3 subscales, these subscale scores were the candidate indicators for the taxometric analysis. The mean, SD, skewness, and kurtosis of all input indicators for the taxometric analysis of the PBQ were calculated (Table\u0026nbsp;5). Among the PBQ subscale scores, the kurtosis of all subscales was high, particularly for RF.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eINSERT TABLE 5 AROUND HERE\u003c/h3\u003e\n\u003cp\u003eAs the candidates for the input indicators of the PBQ were 3 subscales (i.e., AR, LA, and RF scores), we checked Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e between and within-group correlations for the putative taxa and complements of those subscale scores (Table\u0026nbsp;6). Within-group correlations for the putative taxa and complements of the PBQ subscales were 0.25 and 0.11, respectively. All Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e values were greater than 1.25. Finally, 3 indicators (AR, LA, and RF) were used in the taxometric analysis.\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eINSERT TABLE 6 AROUND HERE\u003c/h2\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003eMAXEIG, MAMBAC, and L-Mode analyses\u003c/h2\u003e \u003cp\u003eThe MAMBAC, MAXEIG, L-Mode, and mean CCFI values for each scale are listed in Table\u0026nbsp;7. The mean CCFI of the PBQ was 0.512.\u003c/p\u003e \u003cdiv id=\"Sec33\" class=\"Section4\"\u003e \u003ch2\u003eINSERT TABLE 7 AROUND HERE\u003c/h2\u003e \u003cp\u003e \u003cem\u003eGenerating CCFI profiles\u003c/em\u003e \u003c/p\u003e \u003cp\u003eBecause the PBQ data appeared ambiguous rather than dimensional, we generated the CCFI profiles. All CCFI profiles of the PBQ were below 0.500 (Table\u0026nbsp;8). The mean CCFI profile was 0.446, indicating data dimensionality.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eINSERT TABLE 8 AROUND HERE\u003c/h3\u003e\n\n\u003ch3\u003eDiscussion\u003c/h3\u003e\n\u003cp\u003eStudy 2 showed that the symptoms of bonding disorder (AR, LA, and RF) measured by the PBQ were likely dimensional in nature. The PBQ was originally developed to detect severe bonding disorder cases, such as an abusive parent, in comparison to other scales, including the MIBS. Thus, items such as \u0026lsquo;I have done harmful things to my baby\u0026rsquo; or \u0026lsquo;I feel like hurting my baby\u0026rsquo; that indicated severe symptoms of bonding disorders were included in the PBQ; this was described as pathological anger or established rejection towards one\u0026rsquo;s own baby (Brockington et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). However, these symptoms did not have a categorical structure. Those symptoms may not be at the core in terms of the taxonicity. A different combination of symptoms (indicators) may indicate the presence of taxa.\u003c/p\u003e \u003cdiv id=\"Sec36\" class=\"Section2\"\u003e \u003ch2\u003eStudy 3: SPCE\u003c/h2\u003e \u003cdiv id=\"Sec37\" class=\"Section3\"\u003e \u003ch2\u003eMethods\u003c/h2\u003e \u003cdiv id=\"Sec38\" class=\"Section4\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe sample was obtained from our cross-sectional web survey, which aimed to validate the SPCE (Hada et al., 2023). The participants in this study comprised 780 men and 780 women who were first-time parents and whose child\u0026rsquo;s age ranged from being a foetus to 12 years old. This survey was conducted in cooperation with Cross Marketing Inc. (Shinjuku, Tokyo, Japan) in 2022. Information from a web questionnaire was sent via e-mail to the premise-targeted individuals within the research panels of Cross Marketing Inc. In this study, 729,559 eligible people were estimated to be the recruit premise targets. The response rate was 9.23\u0026ndash;13.23%. We used only women data. As the SPCE had strict measurement equivalence using the item response theory, the stability of its construct was assured across the children\u0026rsquo;s ages. The participants\u0026rsquo; mean (SD) age was 34.3 (8.3) years.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section3\"\u003e \u003ch2\u003eMeasurement\u003c/h2\u003e \u003cp\u003e \u003cem\u003eScale of Parent-to-Child Emotions\u003c/em\u003e (\u003cem\u003eSPCE; Hada et al., 2023\u003c/em\u003e)\u003c/p\u003e \u003cp\u003eThe SPCE is a scale used to measure parents\u0026rsquo; primary emotions with respect to their child. The SPCE consists of 43 items with 9 domains of human emotions: Happiness, Anger, Fear, Sadness, Disgust, Shame, Guilt, Alpha pride, and Beta pride. The items are scored from 0 (\u0026lsquo;did not feel at all\u0026rsquo;) to 6 (\u0026lsquo;felt extremely strongly\u0026rsquo;). The 9 domains can be addressed in 2 groups of positive (Happiness, Alpha pride, and Beta pride) and negative (Anger, Fear, Sadness, Disgust, Shame, and Guilt) emotions (Tanke et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec40\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis used in this study was the same as that in Study 1. As approximately 25% of women had bonding disorder when bonding was measured using the SPCE (Hada, Ohashi et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), we set the base rate of the putative taxon as 0.25 in the taxometric analysis for the SPCE.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResults\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003eIndicator selection\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAs the SPCE has nine (i.e., 3 or more) subscales, these subscale scores could be candidate indicators for the taxometric analysis. The mean, SD, skewness, and kurtosis of all candidates used as input indicators for the taxometric analysis of the SPCE were calculated (Table\u0026nbsp;9). The skewness and kurtosis of the SPCE subscale scores were normally distributed.\u003c/p\u003e\n\u003ch3\u003eINSERT TABLE 9 AROUND HERE\u003c/h3\u003e\n\u003cp\u003eWe then checked Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e between and within-group correlations for the putative taxa and complements of each set of 3 indicators. The candidates for the input indicators of the SPCE were 9 subscales separated into positive and negative groups. Because emotions have positive and negative valences, the SPCE subscales can be separately used with positive (i.e., Happiness, Alpha pride, and Beta pride) and negative (i.e., Anger, Fear, Sadness, Disgust, Shame and Guilt) emotion domains (Hada \u0026amp; Kitamura, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hada, Usui et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kitamura, Hada, et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tanke et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, we checked Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e between and within-group correlations for the putative taxa and complements of Happiness, Alpha pride, and Beta pride, which belonged to the positive emotion domain of the SPCE (Table\u0026nbsp;10). Among the subscales of the negative emotion domain of the SPCE, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e between and within-group correlations for the putative taxa and complements for 20 combinations (\u003csub\u003e6\u003c/sub\u003e\u003cem\u003eC\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e: select 3 indicators from 6 candidate indicators) were checked to determine the appropriate combination as input indicators for the taxometric analysis (Supplemental Table). We selected the combination with a Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;1.25 and the mean within-group correlations for the putative taxon and complement\u0026thinsp;\u0026lt;\u0026thinsp;0.3 (Table\u0026nbsp;11). Finally, we identified Happiness, Beta pride, and Alpha pride for the positive emotion domain of the SPCE, followed by Combination 1 (Anger, Fear, and Sadness), Combination 6 (Shame, Anger, and Fear), Combination 10 (Fear, Disgust, and Shame), Combination 16 (Disgust, Anger, and Fear), Combination 17 (Shame, Anger, and Fear), and Combination 20 (Fear, Disgust, and Guilt) for the negative emotion domain of the SPCE.\u003c/p\u003e\n\u003ch3\u003eINSERT TABLES 10 AND 11 AROUND HERE\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003eMAXEIG, MAMBAC, and L-Mode analyses\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe MAMBAC, MAXEIG, L-mode, and mean CCFI values for each scale are listed in Table\u0026nbsp;12. The mean CCFI for the positive emotion domain of the SPCE was 0.414 (Fig.\u0026nbsp;2). The curves with the results for the positive emotion domains (a dark line) are shown in graphics layered above the results for the categorical and dimensional comparison data. In the graphics, the plots of the middle 50% of the data points are shown as grey bands, and the minimum and maximum values are shown as thin lines. The curves for the positive emotion domains were much closer to those for the dimensional rather than categorical data. Because the positive emotion domains were likely to appear dimensional, the CCFI profiles were omitted from the next step. For the negative emotion domains, the mean CCFIs of Combinations 1 (Anger, Fear, and Sadness), Combination 6 (Guilt, Anger, and Fear), Combination 10 (Fear, Disgust, and Shame), Combination 16 (Disgust, Anger, and Fear), Combination 17 (Shame, Anger, and Fear), and Combination 20 (Fear, Disgust, and Sadness) were 0.645, 0.583, 0.565, 0.605, 0.557, and 0.513, respectively. All mean CCFIs, except for the negative emotion domains, exceeded 0.45, indicating ambiguous or categorical data.\u003c/p\u003e\n\u003ch3\u003eINSERT TABLE 12 AND FIG. 2 AROUND HERE\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003eGenerating CCFI profiles\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe negative emotion domains appeared ambiguous or categorical rather than dimensional, and we generated the CCFI profiles (Table\u0026nbsp;13). The CCFI profiles of MAMBAC, MAXEIG, L-Mode, and mean profile values for Combination 1 (Anger, Fear, and Sadness) were 0.499, 0.654, 0.523, and 0.558, respectively. The combination with the next highest mean CCFI was Combination 17 (Shame, Anger, and Fear), of which the MAMBAC, MAXIEG, L-Mode, and Mean CCFI profile values were 0.583, 0.508, 0.511, and 0.533, respectively. The combination with the third-highest mean CCFI profile was Combination 16 (Disgust, Anger, and Fear), of which the MAMBAC, MAXIEG, L-Mode, and mean CCFI profile values were 0.489, 0.576, 0.488, and 0.517, respectively. The mean CCFI profiles for Combinations 1, 17, and 16 exceeded 0.500. Regarding the mean CCFI profiles for each combination, only Combination 1 for the negative emotion domains exceeded 0.550, indicating categorical data. In terms of the base rate estimation, the \u003cem\u003eRunCCFIProfile()\u003c/em\u003e function provided a mean profile estimate of 0.276 for Combination 1 (Fig.\u0026nbsp;3), 0.266 for Combination 17, and 0.263 for Combination 16. These curves indicated the categorical nature of the data.\u003c/p\u003e\n\u003ch3\u003eINSERT TABLE 13 AND FIG. 3 AROUND HERE\u003c/h3\u003e\n\n\u003ch3\u003eDiscussion\u003c/h3\u003e\n\u003cp\u003eStudy 3 showed that SPCE positive emotion domains (i.e., Happiness, Alpha pride, and Beta pride) indicated the dimensionality of the data. Psychological phenomena such as mothers\u0026rsquo; lack of or excessive intensity of positive emotions towards their child may not be a clinical entity but a matter of degrees. However, the SPCE negative emotion domains, particularly the combination of Anger, Fear and Sadness of the bonding disorder measured by the SPCE, indicated the categorical nature of the data, marked by the highest mean CCFI profile. In addition, the CCFIs for the two combinations of Disgust, Anger, and Fear and Shame, Anger, and Fear were shown to be above 0.500 and were categorical rather than dimensional. Combinations of Anger and Fear may result in pathological symptoms of bonding disorder. Therefore, bonding disorder may deserve to be acknowledged as a clinical entity.\u003c/p\u003e"},{"header":"GENERAL DISCUSSION","content":"\u003cp\u003eTo the best of our knowledge, this study is the first taxometric analysis of maternal bonding. Our findings showed that the CCFI profiles for the combination of Anger, Fear and Sadness of parent-to-child emotions measured by the SPCE suggested the presence of categorical data (i.e., mean CCFI profiles of those above 0.550). Mothers\u0026rsquo; anger, fear, and sadness towards their child are likely to have a latent construct (i.e., a taxon). Hence, we believe that these three maternal emotions towards the child (as a taxon) are core symptoms of mother-to-child bonding disorders. It is of note that the CCFI profiles for the combinations of Shame, Anger and Fear, and that of Disgust, Anger, and Fear showed ambiguous data. Thus, the two indicators (Anger and Fear) may be singled out as core symptoms of bonding disorder. Despite numerous psychological symptoms and personality types being dimensional or ambiguous (Haslam, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Haslam et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), our data on the SPCE\u0026rsquo;s negative domains suggested categorical nature of the concept.\u003c/p\u003e \u003cp\u003eIn contrast to the above results, MIBS, PBQ, and SPCE positive domains suggested the dimensionality of the data. Psychological symptoms measured using these indicators were likely to capture bonding difficulties without the presence of discrete pathological entities. Ruscio et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) pointed out that a dimensional construct may comprise several dimensions, some of which may subsume or be subsumed by dimensions at higher or lower levels within the nested broader construct (p. 16). Further exploration and taxon seeking are required.\u003c/p\u003e \u003cp\u003eThe CCFI profiles for the combination of Anger, Fear and Sadness of parent-to-child emotions measured by the SPCE provided a base rate estimation of 0.276 (i.e., taxon size\u0026thinsp;=\u0026thinsp;27.6%). Surprisingly, the value of the base rate estimation was in accordance with the findings of the typology of parental bonding measured by the SPCE (Hada et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In our previous study, the cases of mothers classified into the bonding disorder cluster were 29.0% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;656/2,264). Bonding disorder cluster cases have shown significantly higher Anger, Fear and Sadness scores than other clusters (Hada et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this line, core symptoms of bonding disorder are likely to be Anger, Fear and Sadness emotions.\u003c/p\u003e \u003cp\u003eFurthermore, additional evidence adds to the literature. The Dimensional Assessment of Mother Baby Organisation Questionnaire-11 (DAMBO-Q11) includes Anger-, Fear-, and Guilt-items elicited from the DAMBO-Q33 (T. and F. Kitamura Foundation for Studies and Skill Advancement in Mental Health). The Anger-, Fear-, and Guilt-items of the DAMBO-Q11 were selected based on the findings showing a high Area Under Curve (AUC) detected antenatal psychological symptoms (APS), including the foetal bonding disorder. Each item from the 9 domains targeting the foetal bonding disorder from the SPCE was adopted into items of the DAMBO-Q33. These items were selected based on the amount of information and item characteristics in terms of the item response theory (IRT) (Kitamura, Yamamoto et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Thus, regardless of whether different methods or populations were used, Anger and Fear were elicited as the core items for bonding disorders.\u003c/p\u003e \u003cp\u003eParts of bonding disorders reflected by the MIBS or PBQ overlap with those in the SPCE. However, the SPCE was created by focusing on the concepts of human emotion (Hada et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Basic emotions have automatic appraisal mechanisms that are not only quick but also occur without awareness (Ekman, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Lazarus, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) and motivate human behaviour. If parental bonding is defined as \u003cem\u003eemotions\u003c/em\u003e such as \u003cem\u003eemotional\u003c/em\u003e ties or \u003cem\u003eaffective\u003c/em\u003e bonds, cognitive or behavioural aspects should be distinguished from them. Kinsey and Hupcey (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) noted the following:\u003c/p\u003e \u003cp\u003eBehavioral and biological indicators may promote maternal\u0026ndash;infant bonding or be an outcome of maternal\u0026ndash;infant bonding, but are not sufficient to determine the quality of maternal\u0026ndash;infant bonding nor are these indicators unique to the concept.\u003c/p\u003e \u003cp\u003eThus, the SPCE is likely to detect categorical and pathological cases in terms of emotional bonding more accurately than the MIBS or PBQ, which include cognitive and behavioural aspects in addition to emotional items.\u003c/p\u003e \u003cp\u003eOur study had several limitations. First, three samples for the taxometric analysis were obtained from different study samples. Although we examined the CCFI profiles, which were less biased and more accurate, the sample with simultaneous assessment by the MIBS, PBQ, and SPCE was ideal. Second, we used patient-reported outcome measures. Interviews or observational data, including structured interviews or laboratory assessments, should be used to assess parent\u0026ndash;child bonding. Third, our study focused only on mother-to-child emotions. Fathers also form affectionate bonds with their children; therefore, further studies simultaneously examining both parents may shed light on the symptomatic structures of parental bonding. Despite these limitations, our findings may contribute to the definition and conceptualisation of (maternal) bonding disorders.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eWe expect the SPCE to become an attractive and promising assessment tool for measuring parental bonding in future research and practice. These efforts would contribute to identifying cases of bonding disorders that require care, thereby promoting parent-to-child bonding.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the guidelines of the Declaration of Helsinki and was approved by the Research Ethics Committee of the Kitamura Institute of Mental Health Tokyo for Samples 1 and 2 (no. 2020030501, 21 March 2020) and Sample 3 (no. 2021101401, 13 November 2021). All participants were informed about the aims of the study, ethical considerations for participation, security of personal information, and affiliation of the principal investigator. Anonymity and voluntary participation were assured. Appropriate informed consent, including an electronic informed consent form, was obtained from all participants involved in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the JSPS KAKENHI (grant number 21H03255; PI: Yukiko Ohashi).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAyako Hada: Conceptualisation, Formal analysis, Methodology, Writing \u0026ndash; original draft.\u003c/p\u003e\n\u003cp\u003eToshinori Kitamura: Conceptualisation, Methodology, Project administration, Supervision, Writing \u0026ndash; review and editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll the authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset analysed and used in this study is available upon reasonable request from the first author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBaba K, Kataoka Y, Kitamura T (2023) Identifying core items of the Japanese version of the Mother-to-Infant Bonding Scale for diagnosing postpartum bonding disorder. Healthcare 11:1740. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/healthcare11121740\u003c/span\u003e\u003cspan address=\"10.3390/healthcare11121740\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaba K, Takauma F, Tada K, Tanaka T, Sakanashi K, Kataoka Y, Ktamura T (2017) Factor structure of the Conflict Tactics Scale. Int J Community Based Nurs Midwifery 5:239\u0026ndash;247\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBranjerdporn G, Meredith P, Strong J, Garcia J (2017) Associations between maternal-foetal attachment and infant developmental outcomes: A systematic review. Matern Child Health J 21(3):540\u0026ndash;553. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10995-016-2138-2\u003c/span\u003e\u003cspan address=\"10.1007/s10995-016-2138-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrockington IF, Aucamp HM, Fraser C (2006) Severe disorders of the mother-infant relationship: definitions and frequency. Arch Women Ment Health 9:243\u0026ndash;251\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrockington IF, Oates J, George S, Turner D, Vostanis P, Sullivan M, Murdoch C (2001) A screening questionnaire for mother-infant bonding disorders. Arch Women Ment Health 3:133\u0026ndash;140\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEkman P (1977) Biological and cultural contributions to body and facial movement. In: Blacking J (ed) Anthropology of the body. Academic, pp 39\u0026ndash;84\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaisal-Cury A, Tabb KM, Ziebold C, Matijasevich A (2021) The impact of postpartum depression and bonding impairment on child development at 12 to 15 months after delivery. J Affect Disorders Rep 4:100125\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHada A, Kitamura T (2024) Factor structures and clusters of psychological symptoms during pregnancy: Proposal of antenatal psychological syndrome. In: Kitamura T (ed) Dimensional Assessment of Mother Baby Organization Project: Many facets of psychological difficulties among expectant women. Nova Publishing, pp 117\u0026ndash;144\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHada A, Kubota C, Imura M, Takauma F, Tada K, Kitamura T (2019) The Edinburgh Postnatal Depression Scale: Model comparison of factor structure and its psychosocial correlates among mothers at one month after childbirth in Japan. Open Family Stud J 11:1\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2174/1874922401911010001\u003c/span\u003e\u003cspan address=\"10.2174/1874922401911010001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHada A, Ohashi Y, Usui Y, Kitamura T (2024) A scale of parent-to‐child emotions: adaptation, factor structure, and measurement invariance. Fam Process 63(3):1677\u0026ndash;1701. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/famp.12919\u003c/span\u003e\u003cspan address=\"10.1111/famp.12919\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHada A, Ohashi Y, Usui Y, Kitamura T (2024) Typology of parent-to-child emotions: A study of Japanese parents of a foetus up to a 12-year-old child. Healthcare 12(9):881. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/healthcare12090881\u003c/span\u003e\u003cspan address=\"10.3390/healthcare12090881\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHada A, Usui Y, Ohashi Y, Takeda S, Kitamura T (2024) Typology of pregnant women\u0026rsquo;s bonding emotions towards their foetus: A study of Japanese women in the first trimester. Psychology 15(3):329\u0026ndash;340\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaslam N (2019) Unicorns, snarks, and personality types: A review of the first 102 taxometric studies of personality. Australian J Psychol 71(1):39\u0026ndash;49\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaslam N, McGrath MJ, Viechtbauer W, Kuppens P (2020) Dimensions over categories: A meta-analysis of taxometric research. Psychol Med 50(9):1418\u0026ndash;1432\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHashijiri K, Watanabe Y, Fukui N, Motegi T, Ogawa M, Egawa J, Someya T (2021) Identification of bonding difficulties in the peripartum period using the Mother-to-Infant Bonding Scale-Japanese Version and its tentative cutoff points. Neuropsychiatr Dis Treat 17:3407\u0026ndash;3413. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2147/NDT.S336819\u003c/span\u003e\u003cspan address=\"10.2147/NDT.S336819\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKendell RE, Brockington IF (1980) The identification of disease entities and the relationship between schizophrenic and affective psychoses. Br J Psychiatry 137(4):324\u0026ndash;331\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKinsey C, Hupcey JE (2013) State of the science of maternal-infant bonding: A principle-based concept analysis. Midwifery 29(12):1314\u0026ndash;1320. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.midw.2012.12.019\u003c/span\u003e\u003cspan address=\"10.1016/j.midw.2012.12.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKitamura T, Hada A, Usui Y, Ohashi Y (2025) Clusters and case vignettes of maternal-foetal bonding disorders: A mixed methods approach. (under review; submited to \u003cem\u003ePsychiatry and Clinical Neurosciences Reports\u003c/em\u003e)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKitamura T, Takauma F, Tada K, Yoshida K, Nakano H (2004) Postnatal depression, social support, and child abuse. World Psychiatry 3:100\u0026ndash;101\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKitamura T, Takegata M, Haruna M, Yoshida K, Yamashita H, Murakami M, Goto Y (2015) The Mother-Infant Bonding Scale: Factor structure and psychosocial correlates of parental bonding disorders in Japan. J Child Fam stud 24:393\u0026ndash;401. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10826-013-9849-4\u003c/span\u003e\u003cspan address=\"10.1007/s10826-013-9849-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKitamura T, Yamamoto M, Saito T, Hada A, Tanke A, Usui Y, Ishida H (2025) Development and validation of a multidimensional mental health screening questionnaire for pregnant women: A preliminary report. Psychiatry Clin Neurosciences Rep 4(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/pcn5.70053\u003c/span\u003e\u003cspan address=\"10.1002/pcn5.70053\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar RC (1997) Anybody's child: Severe disorders of mother-to-infant bonding. Br J Psychiatry 171(2):175\u0026ndash;181\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLazarus RS (1991) Emotion and adaptation. Oxford University Press\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLe Bas GA, Youssef GJ, Macdonald JA, Rossen L, Teague SJ, Kothe EJ, McIntosh JE, Olsson CA, Hutchinson DM (2018) The role of antenatal and postnatal maternal bonding in infant development: A systematic review and meta-analysis. Soc Dev 29:3\u0026ndash;20\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsunaga A, Ohashi Y, Sakanashi K, Kitamura T (2021) Factor structure of the Postpartum Bonding Questionnaire: Configural invariance and measurement invariance across postpartum time periods. J Psychiatr Res 135:1\u0026ndash;7\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsunaga A, Takauma F, Tada K, Kitamura T (2017) Discrete category of mother-to-infant bonding disorder and its identification by the Mother-to-Infant Bonding Scale: A study in Japanese mothers of a 1-month-old. Early Hum Dev 111:1\u0026ndash;5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeehl PE (1965) Detecting latent clinical taxa by fallible quantitative indicators lacking an accepted criterion. Retrieved from the University Digital Conservancy. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hdl.handle.net/11299/151479\u003c/span\u003e\u003cspan address=\"https://hdl.handle.net/11299/151479\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeehl PE (1992) Factors and taxa, traits and types, differences of degree and differences in kind. J Pers 60(1):117\u0026ndash;174. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1467-6494.1992.tb00269.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1467-6494.1992.tb00269.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeehl PE, Yonce LJ (1994) Taxometric analysis: I. Detecting taxonicity with two quantitative indicators using means above and below a sliding cut (MAMBAC procedure). Psychol Rep 74(3):1059\u0026ndash;1274\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeehl PE (1995) Bootstraps taxometrics: Solving the classification problem in psychopathology. Am Psychol 50(4):266\u0026ndash;275\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeehl PE, Yonce LJ (1996) Taxometric analysis: II. Detecting taxonicity using covariance of two quantitative indicators in successive intervals of a third indicator (Maxcov procedure). Psychol Rep 78(3, Pt 2):1091\u0026ndash;1227\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuzik M, Bocknek EL, Broderick A, Richardson P, Rosenblum KL, Thelen K, Seng JS (2013) Mother\u0026ndash;infant bonding impairment across the first 6 months postpartum: The primacy of psychopathology in women with childhood abuse and neglect histories. Arch Women Ment Health 16(1):29\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00737-012-0312-0\u003c/span\u003e\u003cspan address=\"10.1007/s00737-012-0312-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOhashi Y, Takegata M, Haruna M, Kitamura T, Takauma F, Tada K (2015) Association of specific negative life events with depression severity one month after childbirth in community-dwelling mothers. Int J Nurs Health Sci 2:13\u0026ndash;20\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOhashi Y, Kitamura T, Sakanashi K, Tanaka T (2016) Postpartum bonding disorder: factor structure, validity, reliability and a model comparison of the postnatal bonding questionnaire in Japanese mothers of infants. Healthcare 4(3):50\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOhashi Y, Sakanashi K, Tanaka T, Kitamura T (2016) Mother-to-infant bonding disorder, but not depression, 5 days after delivery is a risk factor for neonate emotional abuse: A study in Japanese mothers of 1-month olds. Open Family Stud J 8:27\u0026ndash;36\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR\u0026oslash;hder K, V\u0026aelig;ver MS, Aarestrup AK, Jacobsen RK, Smith-Nielsen J, Schi\u0026oslash;tz ML (2020) Maternal-fetal bonding among pregnant women at psychosocial risk: The roles of adult attachment style, prenatal parental reflective functioning, and depressive symptoms. PLoS ONE, 15(9), e0239208\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuscio J, Carney LM, Dever L, Pliskin M, Wang SB (2018) Using the Comparison Curve Fit Index (CCFI) in taxometric analyses: Averaging curves, standard errors, and CCFI profiles. Psychol Assess 30(6):744\u0026ndash;754\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuscio J, Kaczetow W (2009) Differentiating categories and dimensions: Evaluating the robustness of taxometric analyses. Multivar Behav Res 44:259\u0026ndash;280\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuscio J, Ruscio AM, Haslam N (2006) \u003cem\u003eIntroduction to the taxometric method: A practical guide (1st ed.).\u003c/em\u003e Routledge. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4324/9780203726549\u003c/span\u003e\u003cspan address=\"10.4324/9780203726549\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuscio J, Ruscio AM, Meron M (2007) Applying the bootstrap to taxometric analysis: Generating empirical sampling distributions to help interpret results. Multivar Behav Res 42:349\u0026ndash;386\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuscio J, Walters GD, Marcus DK, Kaczetow W (2010) Comparing the relative fit of categorical and dimensional latent variable models using consistency tests. Psychol Assess 22(1):5\u0026ndash;21\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuscio J, Ruscio AM, Carney LM (2011) Performing taxometric analysis to distinguish categorical and dimensional variables. J Experimental Psychopathol 2(2):170196\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuscio J, Wang SB (2021) RTaxometrics: Taxometric Analysis. R package version 3.2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/package=RTaxometrics\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/package=RTaxometrics\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuscio J, Wang SB (2022) Taxometric analysis. In G. J. G. Asmundson (Ed.), \u003cem\u003eComprehensive Clinical Psychology. (2nd Ed.), Vol. 3\u003c/em\u003e, pp. 148\u0026ndash;175. New York: Elsevier\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSims A (1988) Symptoms in the mind. Paris, France, Bailli\u0026egrave;re Tindall\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStraus MA, Hamby SL, Finkelhor D, Moore DW, Runyan D (1998) Identification of child maltreatment with the Parent-Child Conflict Tactics Scales: Development and psychometric data for a national sample of American parents. Child Abuse Negl 22(4):249\u0026ndash;270. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0145-2134(97)00174-9\u003c/span\u003e\u003cspan address=\"10.1016/S0145-2134(97)00174-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eT. and F. Kitamura Foundation for Studies and Skill Advancement in Mental Health. (2022) \u003cem\u003e33-item Dimensional Assessment of Mother Baby Organization Questionnaire\u003c/em\u003e. Author, Tokyo. (available as an e-book)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrombetta T, Giordano M, Santoniccolo F, Vismara L, Della Vedova AM, Roll\u0026egrave; L (2021) Pre-natal attachment and parent-to-infant attachment: A systematic review. Front Psychol 12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpsyg.2021.620942\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2021.620942\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanke A, Hada A, Kitamra T (2024) Maternal-foetal bonding disorder: Factor structure and correlates. In: Kitamura T (ed) Dimensional Assessment of Mother Baby Organization Project: Many facets of psychological difficulties among expectant women. Nova Publishing, pp 49\u0026ndash;66\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamamoto M, Takauma F, Tada K, Baba K, Kitamura T (2023) Factor Structure and Measurement Invariance of the Japanese Version of the Mother-to-Infant Bonding Scale. Adv Reproductive Sci 11(4):159\u0026ndash;170\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoshida K, Yamashita H, Conroy S, Marks M, Kumar C (2012) A Japanese version of Mother-to-Infant Bonding Scale: Factor structure, longitudinal changes and links with maternal mood during the early postnatal period in Japanese mothers. Arch Women Ment Health 15(5):343\u0026ndash;352. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00737-012-0291-1\u003c/span\u003e\u003cspan address=\"10.1007/s00737-012-0291-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaller NG, Meehl PE (1998) Multivariate taxometric procedures: Distinguishing types from continua. Sage Publications, Inc.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 13 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"53dca1ce-5e0c-46db-b0f6-3c2ae1c74a44","identifier":"10.13039/501100001691","name":"Japan Society for the Promotion of Science","awardNumber":"21H03255","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Perinatal bonding disorder, taxometric analysis, core symptoms, Mother-to-Infant Bonding Scale, Postpartum Bonding Questionnaire, Scale of Parent-to-Child Emotions","lastPublishedDoi":"10.21203/rs.3.rs-6858320/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6858320/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eA bonding disorder refers to the impaired or disordered emotional bond of parent to child has been viewed as pathological: bonding disorder. However, the core symptoms of this disorder have not yet been identified.\u003c/p\u003e\u003ch2\u003eAim\u003c/h2\u003e \u003cp\u003eThis study aimed to identify the combination of symptoms that showed the greatest degree of taxonicity.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA taxometric analysis was conducted using three samples of cross-sectional data from previous studies. Study 1 included the Japanese version of the Mother-to-Infant Bonding Scale, Study 2 included the Postpartum Bonding Questionnaire, and Study 3 included the Scale of Parent-to-Child Emotions (SPCE). The input indicators for the taxometric analysis were selected by checking whether they met the requirements for the analysis. After performing the mean above minus below a cut (MAMBAC), MAXimum EIGen value (MAXEIG), and latent mode (L-Mode) analyses, the comparison curve fit index (CCFI) profiles were obtained when the taxometric results appeared categorical.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe CCFI profiles (i.e., mean CCFI profiles of those showed above 0.550) for the combination of the Anger, Fear and Sadness of parent-to-child emotions measured by the SPCE suggested that the data were categorical.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe SPCE can be considered an attractive and effective assessment tool for measuring parental bonding in future research and clinical practice. (175 words)\u003c/p\u003e","manuscriptTitle":"Searching the core symptoms of perinatal bonding disorders: A series of taxometric analyses for three bonding scales","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 18:19:25","doi":"10.21203/rs.3.rs-6858320/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bd963910-1cd1-4166-ae55-9f391f55b458","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-16T18:19:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-16 18:19:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6858320","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6858320","identity":"rs-6858320","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}