--- title: "Stigma, Barriers, and Attitudes Toward Professional Psychological Help-Seeking Among Young Czech Adults" bibliography: references.bib format: apaquarto-html: css: muni-style.css apaquarto-docx: default self-contained: true echo: false warning: false message: false --- ```{r} #| label: import #| output: false library(targets) library(tidyverse) library(gt) library(lavaan) library(psych) upstore <- here::here("_targets") tar_source() tar_source("../../R") tar_source("../../projects/valireli/R") new_folder("tables") new_folder("figures") # ── Read targets ────────────────────────────────────────────────────────────── smpl <- tar_read(sh_sample_table, store = upstore) scldsc <- tar_read(sh_scale_descriptives, store = upstore) hstab <- tar_read(sh_helpseeking_table, store = upstore) reltab <- tar_read(sh_reliability_table, store = upstore) scorcor <- tar_read(sh_scale_correlations, store = upstore) grpdif <- tar_read(sh_group_differences_table, store = upstore) bingrp <- tar_read(sh_binary_differences, store = upstore) ordgrp <- tar_read(sh_ordinal_differences, store = upstore) binplot <- tar_read(sh_binary_plot, store = upstore) ordplot <- tar_read(sh_ordinal_plot, store = upstore) thrplot <- tar_read(sh_therapy_plot, store = upstore) semfit <- tar_read(sh_sem, store = upstore) semtab <- tar_read(sh_sem_table_main, store = upstore) semtab2 <- tar_read(sh_sem_table_full, store = upstore) modfit <- tar_read(sh_moderation_sem, store = upstore) modtab <- tar_read(sh_moderation_table, store = upstore) sumdat <- tar_read(sh_sum_scores, store = upstore) # ── Convenience pullers ─────────────────────────────────────────────────────── bc <- function(grp, sc, col) { bingrp |> dplyr::filter(groupvar == grp, scale == sc) |> dplyr::pull({{ col }}) } oc <- function(grp, sc, col) { ordgrp |> dplyr::filter(groupvar == grp, scale == sc) |> dplyr::pull({{ col }}) } # ── Sample sizes ────────────────────────────────────────────────────────────── N_total <- nrow(sumdat) N_women <- sum(sumdat$Gender == "Woman", na.rm = TRUE) N_men <- sum(sumdat$Gender == "Man", na.rm = TRUE) N_es <- sum(sumdat$Education == "Elementary school", na.rm = TRUE) N_hs <- sum(sumdat$Education == "High school", na.rm = TRUE) N_ba <- sum(sumdat$Education == "Bachelors", na.rm = TRUE) N_ma <- sum(sumdat$Education == "Masters", na.rm = TRUE) N_phd <- sum(sumdat$Education == "PhD", na.rm = TRUE) age_min <- min(sumdat$Age, na.rm = TRUE) age_max <- max(sumdat$Age, na.rm = TRUE) age_msd <- sprintf("%.2f \u00b1 %.2f", round(mean(sumdat$Age, na.rm = TRUE), 2), round(sd(sumdat$Age, na.rm = TRUE), 2)) # ── Therapy frequencies ─────────────────────────────────────────────────────── past_yes <- mean(sumdat$`Past therapy` == "Yes", na.rm = TRUE) * 100 past_no <- mean(sumdat$`Past therapy` == "No", na.rm = TRUE) * 100 cons_yes <- mean(sumdat$`Thought of therapy` == "Yes" & sumdat$`Past therapy` == "No", na.rm = TRUE) * 100 # ── Reliability ─────────────────────────────────────────────────────────────── alph_obj <- tar_read(sh_alphas, store = upstore) alpha_val <- \(sc) round(alph_obj[[sc]]$total$std.alpha, 2) alpha_psosh <- alpha_val("PSOSH") alpha_ssosh <- alpha_val("SSOSH") alpha_atspph <- alpha_val("ATSPPH-SF") alpha_bhss <- alpha_val("BHSS") # ── SEM parameter pulls ─────────────────────────────────────────────────────── pe <- lavaan::parameterEstimates(semfit, boot.ci.type = "perc", level = 0.95, ci = TRUE, standardized = TRUE) gp <- function(lhs, rhs) pe[pe$lhs == lhs & pe$rhs == rhs & pe$op == "~", ] gl <- function(lbl) pe[pe$label == lbl, ] dir_c1 <- gp("SSOSH", "PSOSH") dir_b1 <- gp("ATSPPH", "SSOSH") dir_b2 <- gp("BHSS", "SSOSH") dir_d1 <- gp("ATSPPH", "PSOSH") dir_d2 <- gp("BHSS", "PSOSH") ind_at <- gl("ind_PSOSH_ATSPPH") ind_bh <- gl("ind_PSOSH_BHSS") sig_test <- function(p, yes, no, na_text = "indeterminate") if (is.na(p)) na_text else if (p < .05) yes else no ``` # Methods ## Participants and Procedure Participants were young Czech adults aged 18 to 35 years, recruited through an online survey distributed via social media, university networks, and snowball sampling. Data collection took place between \[DATE\] and \[DATE\]. Participation was voluntary, anonymous, and without compensation. The study was approved by \[ETHICS BOARD\]. Respondents who did not provide valid responses to any of the four primary scales were excluded from the respective scale analysis but retained for other analyses, consistent with full-information estimation procedures. ## Statistical Analyses Demographic and scale variables were described via frequency tables with proportions (nominal/ordinal) and mean ± SD (continuous). Group differences in scale scores between binary groups (gender, past therapy, considered-but-did-not-seek-help, pleasantness and helpfulness of therapy experience) were evaluated with Welch's *t*-test and Cohen's *d*. Differences across ordered categorical groups (education, town size) were evaluated with the Kruskal-Wallis *H* statistic and $\varepsilon^2$ as a rank-based effect-size index, with Holm-adjusted pairwise Wilcoxon tests for post-hoc comparisons. All comparisons are additionally visualised as violin plots with means and 95% CIs. Internal consistency of all scales and BHSS subscales was evaluated via Cronbach's $\alpha$ [@cronbach1951], computed with 95% confidence intervals following @feldt1987, and via McDonald's hierarchical $\omega_H$ and total $\omega_T$ using the `psych` R package [@psych]. Scales were considered adequately consistent if $\alpha$ exceeded 0.70. The correlational structure among all scale sum scores, age, and help-seeking indicators was explored using Spearman's $\rho$, which is robust to the bounded, typically skewed distribution of Likert sum scores. To address RQ1, scales' scores were compared between sexes, education levels, town sizes, and conditional on past therapy experience, consideration of seeking therapy (considered-to-seek-help status), and presence of mental disorder in self or family. Furthermore, within the sample of participants who underwent therapy, further comparisons were conducted between those that considered it a pleasant or helpful experience, and the type of professional the participants visited (psychotherapist or psychologist). Given the descriptive nature of RQ1, no formal statistical modelling was performed. To address RQ2, we fitted a structural equation model (SEM) based on the Internalized Stigma Model [@vogel2007], in which public stigma (PSOSH) exerts its effect on self-stigma (SSOSH) which directly acts upon the willingness to seek support via affecting the attitudes toward help-seeking (ATSPPH-SF). Moreover, perceived barriers (BHSS) were considered as a separate mediator of the effect of stigma perception on the willingness to seek help, parallel to the ATSPPH-SF pathway. While the original model assumes that the direct effect of PSOSH on ATSPPH-SF is negligible, it remains unclear whether the same holds for the direct effect of PSOSH on BHSS. Consequently, we estimated both direct and indirect effects (@fig-dag). Each endogenous variable was further regressed on gender, age and education as potential confounders (@fig-dag-full). Finally, residual covariance bewtween ATSPP-SF and BHSS was allowed to vary. Since the willingness to seek support was not directly measured, we focus on estimating the direct and indirect path strengths between stigma variables (PSOSH, SOSH) and their mediators, attitudes towards help-seeking (ATSPP-SF) and perceived barriers (BHSS). To test gender moderation (RQ3), the path model was refitted as a multi-group SEM with gender as the grouping variable. ![Directed acyclic graph depicting the hypothesized causal structure. Public stigma (PSOSH) influences attitudes toward help-seeking (ATSPPH-SF) and barriers (BHSS) both directly and indirectly via self-stigma (SSOSH). Help seeking behavior is the final outcome which was not measured in this study. Gender, education and age act as exogenous covariates on all endogenous nodes but are excluded from this depiction for clarity.](../projects/stigma/figures/dag_stighelp_reduced.jpg){#fig-dag} The model was estimated in `lavaan` [@rosseel2012] with the maximum-likelihood estimator using full-information maximum likelihood (FIML) to handle missing data under the assumption that data are missing at random (MAR). Indirect effects and their 95% percentile bootstrap confidence intervals were estimated from 2,000 bootstrap replications. Because the model includes direct paths from PSOSH to both ATSPPH-SF and BHSS alongside the indirect pathways, it is exactly identified (i.e., saturated, *df* = 0). Global fit indices are therefore trivially perfect regardless of the data and non-informative. Instead, inference was based exclusively on the structural parameter estimates and their bootstrap confidence intervals. All analyses were conducted in R (version `r with(version, paste(major, minor, sep = '.'))`). The complete model specification, and analysis code are available at . ------------------------------------------------------------------------ # Results ## Sample Description In total, the sample comprised `r N_total` young Czech adults (mean age `r age_msd` years, range `r age_min`–`r age_max`). There were `r N_women` women (`r round(N_women/N_total*100, 1)`%), and `r N_men` men (`r round(N_men/N_total*100, 1)`%). In terms of educational attainment, `r N_es` finished elementary school only, `r N_hs` participants held a high-school diploma, `r N_ba` a Bachelor's degree, `r N_ma` a Master's degree, and `r N_phd` a doctoral degree. Full demographic and scale descriptive statistics are presented in @tbl-sample. `r round(past_yes, 1)`% of participants reported having previously sought professional psychological help from a psychologist or psychotherapist, while `r round(past_no, 1)`% had not. Among those who had never sought help, `r round(cons_yes, 1)`% reported having considered doing so at some point but ultimately not pursuing it. The distribution of help-seeking indicators, type of provider consulted, and experience ratings are presented in @tbl-helpseeking. ## Scales Internal Consistency ```{r} #| label: reliability-inline alpha_psosh <- alpha_val("PSOSH") alpha_ssosh <- alpha_val("SSOSH") alpha_atspph <- alpha_val("ATSPPH-SF") alpha_bhss <- alpha_val("BHSS") ``` Scale descriptive statistics are presented in @tbl-scales. All scales demonstrated adequate internal consistency in the current sample. Standardized Cronbach's $\alpha$ estimates were `r alpha_psosh` for PSOSH, `r alpha_ssosh` for SSOSH, `r alpha_atspph` for ATSPPH-SF, and `r alpha_bhss` for the BHSS total score. Internal consistency estimates for all BHSS subscales and McDonald's $\omega$ are reported in @tbl-reliability. ```{r} #| label: tbl-scales #| tbl-cap: "Scale sum-score descriptive statistics" gtsave(scldsc, here::here("tables", "tbl1_scale_descriptives.docx")) scldsc ``` ```{r} #| label: tbl-reliability #| tbl-cap: "Cronbach's alpha and McDonald's omega for all scales and subscales" gtsave(reltab, here::here("tables", "tbl2_reliability.docx")) reltab ``` ## Correlational Structure Spearman correlation coefficients among scale sum scores and key covariates are presented in @tbl-correlations. Public stigma (PSOSH) and self-stigma (SSOSH) were positively and significantly correlated with each other and with BHSS total and all BHSS subscales, and negatively correlated with ATSPPH-SF. Age and education showed comparatively weaker associations with the scale scores. ```{r} #| label: tbl-correlations #| tbl-cap: "Spearman correlations among scale sum scores, age, and help-seeking indicators" r_tbl <- round(scorcor$r, 2) p_tbl <- scorcor$p stars <- matrix("", nrow(p_tbl), ncol(p_tbl), dimnames = dimnames(p_tbl)) stars[p_tbl < .05] <- "*" stars[p_tbl < .01] <- "**" stars[p_tbl < .001] <- "***" r_chr <- matrix( #sprintf("%.2f%s", r_tbl, stars), sprintf("%.2f", r_tbl), nrow = nrow(r_tbl), dimnames = dimnames(r_tbl) ) r_chr[upper.tri(r_chr)] <- NA cor_df <- as.data.frame(r_chr) |> tibble::rownames_to_column("Variable") |> gt::gt(rowname_col = "Variable") |> gt::sub_missing(missing_text = "") |> gt::tab_source_note( "Lower triangle: Spearman's \u03c1. Blank cells: not significantly different from zero at p < .05 (two-sided, Holm-corrected). PSOSH: public stigma; SSOSH: self-stigma; ATSPPH-SF: attitudes toward help-seeking; BHSS: barriers." ) gtsave(cor_df, here::here("tables", "tbl3_correlations.docx")) cor_df ``` ## Group Differences Full numeric results for all scales and grouping variables are in @tbl-groups. @fig-binary-groups and @fig-ordinal-groups show distributions and means. @fig-therapy-groups shows comparisons of participants who underwent therapy in the past by their subjective experience. ```{r} #| label: fig-binary-groups #| fig-cap: "Scale distributions by binary grouping variables. Violin shape shows the full distribution; point = mean; range = 95% CI. Significance from Welch t-tests (***p < .001, **p < .01, *p < .05, ns)." #| fig-height: 9 binplot ``` ### Gender ```{r} #| label: gender-inline t_ssosh <- bc("Gender", "SSOSH", t) |> rprint(2) df_ssosh <- bc("Gender", "SSOSH", df) |> rprint(1) p_ssosh <- bc("Gender", "SSOSH", p) |> pprint(3, TRUE) d_ssosh <- bc("Gender", "SSOSH", d) |> rprint(2) M0_ssosh <- bc("Gender", "SSOSH", M_0) |> rprint(2) M1_ssosh <- bc("Gender", "SSOSH", M_1) |> rprint(2) t_psosh <- bc("Gender", "PSOSH", t) |> rprint(2) df_psosh <- bc("Gender", "PSOSH", df) |> rprint(1) p_psosh <- bc("Gender", "PSOSH", p) |> pprint(3, TRUE) d_psosh <- bc("Gender", "PSOSH", d) |> rprint(2) M0_psosh <- bc("Gender", "PSOSH", M_0) |> rprint(2) M1_psosh <- bc("Gender", "PSOSH", M_1) |> rprint(2) t_atspph <- bc("Gender", "ATSPPH-SF", t) |> rprint(2) df_atspph <- bc("Gender", "ATSPPH-SF", df) |> rprint(1) p_atspph <- bc("Gender", "ATSPPH-SF", p) |> pprint(3, TRUE) d_atspph <- bc("Gender", "ATSPPH-SF", d) |> rprint(2) M0_atspph <- bc("Gender", "ATSPPH-SF", M_0) |> rprint(2) M1_atspph <- bc("Gender", "ATSPPH-SF", M_1) |> rprint(2) t_bhss <- bc("Gender", "BHSS", t) |> rprint(2) df_bhss <- bc("Gender", "BHSS", df) |> rprint(1) p_bhss_num <- bc("Gender", "BHSS", p) p_bhss <- p_bhss_num |> pprint(3, TRUE) d_bhss <- bc("Gender", "BHSS", d) |> rprint(2) M0_bhss <- bc("Gender", "BHSS", M_0) |> rprint(2) M1_bhss <- bc("Gender", "BHSS", M_1) |> rprint(2) ``` Men reported significantly higher self-stigma ($M_\text{women}$ = `r M0_ssosh`, $M_\text{men}$ = `r M1_ssosh`; *t*(`r df_ssosh`) = `r t_ssosh`, *p* `r p_ssosh`, *d* = `r d_ssosh`), but not significantly higher public stigma ($M_\text{women}$ = `r M0_psosh`, $M_\text{men}$ = `r M1_psosh`; *t*(`r df_psosh`) = `r t_psosh`, *p* `r p_psosh`, *d* = `r d_psosh`) compared to women. Women reported significantly more positive attitudes toward help-seeking ($M_\text{women}$ = `r M0_atspph`, $M_\text{men}$ = `r M1_atspph`; *t*(`r df_atspph`) = `r t_atspph`, *p* `r p_atspph`, *d* = `r d_atspph`). Gender differences on the BHSS total score were `r if(p_bhss_num < .05) "statistically significant" else "not statistically significant"` ($M_\text{women}$ = `r M0_bhss`, $M_\text{men}$ = `r M1_bhss`; *t*(`r df_bhss`) = `r t_bhss`, *p* `r p_bhss`, *d* = `r d_bhss`). ### Therapy Experience ```{r} #| label: therapy-inline t_ssosh_th <- bc("Past therapy", "SSOSH", t) |> rprint(2) df_ssosh_th <- bc("Past therapy", "SSOSH", df) |> rprint(1) p_ssosh_th <- bc("Past therapy", "SSOSH", p) |> pprint(3, TRUE) d_ssosh_th <- bc("Past therapy", "SSOSH", d) |> rprint(2) M0_ssosh_th <- bc("Past therapy", "SSOSH", M_0) |> rprint(2) M1_ssosh_th <- bc("Past therapy", "SSOSH", M_1) |> rprint(2) t_psosh_th <- bc("Past therapy", "PSOSH", t) |> rprint(2) df_psosh_th <- bc("Past therapy", "PSOSH", df) |> rprint(2) p_psosh_th <- bc("Past therapy", "PSOSH", p) |> pprint(3, TRUE) d_psosh_th <- bc("Past therapy", "PSOSH", d) |> rprint(2) M0_psosh_th <- bc("Past therapy", "PSOSH", M_0) |> rprint(2) M1_psosh_th <- bc("Past therapy", "PSOSH", M_1) |> rprint(2) t_at_th <- bc("Past therapy", "ATSPPH-SF", t) |> rprint(2) df_at_th <- bc("Past therapy", "ATSPPH-SF", df) |> rprint(1) p_at_th <- bc("Past therapy", "ATSPPH-SF", p) |> pprint(3, TRUE) d_at_th <- bc("Past therapy", "ATSPPH-SF", d) |> rprint(2) M0_at_th <- bc("Past therapy", "ATSPPH-SF", M_0) |> rprint(2) M1_at_th <- bc("Past therapy", "ATSPPH-SF", M_1) |> rprint(2) t_bar_th <- bc("Past therapy", "BHSS", t) |> rprint(2) df_bar_th <- bc("Past therapy", "BHSS", df) |> rprint(1) p_bar_th <- bc("Past therapy", "BHSS", p) |> pprint(3, TRUE) d_bar_th <- bc("Past therapy", "BHSS", d) |> rprint(2) M0_bar_th <- bc("Past therapy", "BHSS", M_0) |> rprint(2) M1_bar_th <- bc("Past therapy", "BHSS", M_1) |> rprint(2) ``` Participants with prior therapy experience reported significantly lower self-stigma ($M_\text{no}$ = `r M0_ssosh_th`, $M_\text{yes}$ = `r M1_ssosh_th`; *t*(`r df_ssosh_th`) = `r t_ssosh_th`, *p* `r p_ssosh_th`, *d* = `r d_ssosh_th`), more positive help-seeking attitudes ($M_\text{no}$ = `r M0_at_th`, $M_\text{yes}$ = `r M1_at_th`; *t*(`r df_at_th`) = `r t_at_th`, *p* `r p_at_th`, *d* = `r d_at_th`), and less perceived barriers towards help-seeking ($M_\text{no}$ = `r M0_bar_th`, $M_\text{yes}$ = `r M1_bar_th`; *t*(`r df_bar_th`) = `r t_bar_th`, *p* `r p_bar_th`, *d* = `r d_bar_th`) than those without prior experience. No statistically significant difference was observed regarding public stigma ($M_\text{no}$ = `r M0_psosh_th`, $M_\text{yes}$ = `r M1_psosh_th`; *t*(`r df_psosh_th`) = `r t_psosh_th`, *p* `r p_psosh_th`, *d* = `r d_psosh_th`). Among those with prior therapy experience, both pleasantness and perceived helpfulness of the therapy experience were generally associated with less stigma, less perceived barriers, and more favorable attitudes towards help-seeking (@fig-therapy-groups, @tbl-groups). ### Help-Seeking Consideration ```{r} #| label: considered-inline t_ssosh_cs <- bc("Thought of therapy", "SSOSH", t) |> rprint(2) df_ssosh_cs <- bc("Thought of therapy", "SSOSH", df) |> rprint(1) p_ssosh_cs <- bc("Thought of therapy", "SSOSH", p) |> pprint(3, TRUE) d_ssosh_cs <- bc("Thought of therapy", "SSOSH", d) |> rprint(2) t_psosh_cs <- bc("Thought of therapy", "PSOSH", t) |> rprint(2) df_psosh_cs <- bc("Thought of therapy", "PSOSH", df) |> rprint(1) p_psosh_cs <- bc("Thought of therapy", "PSOSH", p) |> pprint(3, TRUE) d_psosh_cs <- bc("Thought of therapy", "PSOSH", d) |> rprint(2) t_at_cs <- bc("Thought of therapy", "ATSPPH-SF", t) |> rprint(2) df_at_cs <- bc("Thought of therapy", "ATSPPH-SF", df) |> rprint(1) p_at_cs <- bc("Thought of therapy", "ATSPPH-SF", p) |> pprint(3, TRUE) d_at_cs <- bc("Thought of therapy", "ATSPPH-SF", d) |> rprint(2) t_bhss_cs <- bc("Thought of therapy", "BHSS", t) |> rprint(2) df_bhss_cs <- bc("Thought of therapy", "BHSS", df) |> rprint(1) p_bhss_cs <- bc("Thought of therapy", "BHSS", p) |> pprint(3, TRUE) d_bhss_cs <- bc("Thought of therapy", "BHSS", d) |> rprint(2) ``` Compared to participants who had never considered seeking professional help, those who had considered pursuing it showed significantly higher self-stigma (*t*(`r df_ssosh_cs`) = `r t_ssosh_cs`, *p* `r p_ssosh_cs`, *d* = `r d_ssosh_cs`), public stigma (*t*(`r df_psosh_cs`) = `r t_psosh_cs`, *p* `r p_psosh_cs`, *d* = `r d_psosh_cs`), and perceived barriers (*t*(`r df_bhss_cs`) = `r t_bhss_cs`, *p* `r p_bhss_cs`, *d* = `r d_bhss_cs`), as well as less favorable attitudes towards seeking help (*t*(`r df_at_cs`) = `r t_at_cs`, *p* `r p_at_cs`, *d* = `r d_at_cs`). ### Education and City Size ```{r} #| label: edu-inline H_ssosh_edu <- oc("Education", "SSOSH", H) |> rprint(2) df_ssosh_edu <- oc("Education", "SSOSH", df) |> rprint(2) p_ssosh_edu <- oc("Education", "SSOSH", p) |> pprint(3, TRUE) e_ssosh_edu <- oc("Education", "SSOSH", epsilon_sq) |> rprint(2) H_atspph_edu <- oc("Education", "ATSPPH-SF", H) |> rprint(2) df_atspph_edu <- oc("Education", "ATSPPH-SF", df) |> rprint(2) p_atspph_edu <- oc("Education", "ATSPPH-SF", p) |> pprint(3, TRUE) e_atspph_edu <- oc("Education", "ATSPPH-SF", epsilon_sq) |> rprint(2) ``` Relationships between both education level and city size, and all measured psychological constructs were detected. Higher education and larger city size were associated with less public and self-stigma, less perceived barriers, and more favorable attitudes toward help-seeking (see @fig-ordinal-groups and @tbl-groups) ## Mediation Analysis: Attitudes and Barriers as Parallel Mediators The path model is exactly identified (*df* = 0), including both indirect pathways via SSOSH and direct paths from PSOSH to each outcome exhausts all model degrees of freedom, so global fit statistics are trivially perfect and uninformative. Inference is therefore based on the structural parameter estimates and their bootstrap confidence intervals. The direct effect of public stigma on self-stigma was $\beta$ = `r rprint(dir_c1$std.all, 2)`, 95% CI \[`r rprint(dir_c1$ci.lower, 2)`, `r rprint(dir_c1$ci.upper, 2)`\]. The direct effect of self-stigma on attitudes was $\beta$ = `r rprint(dir_b1$std.all, 2)`, 95% CI \[`r rprint(dir_b1$ci.lower, 2)`, `r rprint(dir_b1$ci.upper, 2)`\], and the direct effect of self-stigma on barriers was $\beta$ = `r rprint(dir_b2$std.all, 2)`, 95% CI \[`r rprint(dir_b2$ci.lower, 2)`, `r rprint(dir_b2$ci.upper, 2)`\]. The indirect effect of PSOSH on ATSPPH-SF via SSOSH was $\beta$ = `r rprint(ind_at$std.all, 3)`, 95% CI \[`r rprint(ind_at$ci.lower, 2)`, `r rprint(ind_at$ci.upper, 2)`\], whereas the equivalent effect on BHSS was $\beta$ = `r rprint(ind_bh$std.all, 2)`, 95% CI \[`r rprint(ind_bh$ci.lower, 2)`, `r rprint(ind_bh$ci.upper, 2)`\]. These findings are `r if(ind_at$ci.lower > 0 | ind_at$ci.upper < 0) "consistent" else "not fully consistent"` with the Internalized Stigma Model's assumption that public stigma exerts its influence on attitudes towards help-seeking behavior via the internalization of stigmatizing beliefs. Full parameter estimates excluding covariate effects are presented in @tbl-sem (see @tbl-sem-full for estimates including covariate effects). ```{r} #| label: tbl-sem #| tbl-cap: "SEM parameter estimates (excluding covariates effects)" gtsave(semtab, here::here("tables", "tbl4_sem.docx")) semtab ``` ## Gender Moderation of the Mediation Structure Model parameters of the gender moderation model are presented in @tbl-moderation. Overall, the primary findings of the main model hold in the gender-specific submodels. # Appendix ![Directed acyclic graph depicting the hypothesized causal structure. Public stigma (PSOSH) influences attitudes toward help-seeking (ATSPPH-SF) and barriers (BHSS) both directly and indirectly via self-stigma (SSOSH). Help seeking behavior is the final outcome which was not measured in this study. Gender, education and age act as exogenous covariates on all endogenous nodes.](../projects/stigma/figures/dag_stighelp_full.jpg){#fig-dag-full} ```{r} #| label: tbl-sample #| tbl-cap: "Demographic characteristics of the sample" gtsave(smpl$categorical, here::here("tables", "tblA1_sample_categorical.docx")) smpl$categorical ``` ```{r} #| label: tbl-helpseeking #| tbl-cap: "Help-seeking patterns." gtsave(hstab, here::here("tables", "tblA2_helpseeking.docx")) hstab ``` ```{r} #| label: tbl-groups #| tbl-cap: "Group comparisons." gtsave(grpdif, here::here("tables", "tblA3_group_differences.docx")) grpdif ``` ```{r} #| label: fig-ordinal-groups #| fig-cap: "Scale distributions by ordinal grouping variables. Point = mean; range = 95% CI. KW: overall Kruskal-Wallis significance; post-hoc pairwise results in Table 6." #| fig-height: 6 ordplot ``` ```{r} #| label: fig-therapy-groups #| fig-cap: "Scale distributions by therapy experience (past-therapy subsample only). Point = mean; range = 95% CI. Significance from Welch t-tests (***p < .001, **p < .01, *p < .05, ns)." #| fig-height: 6 thrplot ``` ```{r} #| label: tbl-sem-full #| tbl-cap: "SEM parameter estimates including covariate effects" gtsave(semtab2, here::here("tables", "tblA4_sem.docx")) semtab2 ``` ```{r} #| label: tbl-moderation #| tbl-cap: "Gender-moderation SEM" gtsave(modtab, here::here("tables", "tblA5_moderation.docx")) modtab ``` # References