A NALYSES

The IBM SPSS for Windows version 23 (IBM, Corp., Armonk, NY) and R 3.3 [184]

with the packages lavaan 0.5 [185] and nlme (Linear mixed-effect (LME) model) [186] were employed for statistical analyses in Paper I. The IBM SPSS for Windows version 23 (IBM Corp., Armonk, NY) and STATA 14 (Stata-Corp., College Station, TX) [187] were used in Paper II. The IBM SPSS for Windows version 24 (IBM Corp., Armonk, NY) [188] and STATA 15 (Stata Corp., College Station, TX) [189] were utilised in Paper III. The tests were two-tailed, and the level of significance was set to 0.05 for all papers, as Bonferroni correction applied for the α-level in models

involving multiple tests was considered too conservative.

3.4.1 Paper I

In order to assess the psychometric properties of the Norwegian version of the RCS a confirmatory factor analysis (CFA) was employed evaluating four estimates of model fit; comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA) and standardised root mean square residual (SRMS). To define a satisfactory model fit, the following criteria were utilised: CFI ≥ 0.95, TLI ≥ 0.95, RMSEA < 0.06 to 0.08 and SRMR ≤ 0.08 [190-192]. A χ² difference test was employed to assess the best model fit [193].

Cronbach’s alpha (α) was computed to assess intra-scale consistency with regards to internal reliability; Cronbach’s α ≥ 0.90 was regarded as excellent [194].

At the time of conducting this study, no instruments measuring communication and relationships in teams were available in Norwegian language. Therefore, the criterion validity of the RCS could not be tested according to what degree the subscale scores were reflecting a golden standard [195].

The arrangement of RCS items in Corporator Surveyor allowed responders to leave items unanswered, and consequently the questionnaire was returned with missing items in Paper I. Therefore, cases were excluded due to missing data. Missing RCS responses were handled by excluding respondents with less than 40% of the

questionnaire filled in. Furthermore, health care professionals who responded to less than three of the seven RCS items were excluded. Finally, teams were excluded if the care process had less than four respondents.

In order to explore the research question concerning team functions (communication and relationships) within and between interprofessional team members in specific care processes in secondary health care, a one-way analysis of variance (ANOVA) was utilised.

To assess individual- and team-level characteristics influencing communication and relationships in the interprofessional team, linear models were employed. To analyse individual-level characteristics, LME models were employed using age, sex, use of clinical procedures and profession as independent variables and RCS subscale scores as dependent variables. Team affiliation was set as a random effect in the LME model to account for possible intra-cluster correlations within each team. Simple linear models were employed to analyse team-level characteristics. In these models, aspects concerning the composition of the team (proportion of women, proportion of

physicians, team members above 40 years and team size) and the use of written clinical procedure were included as independent variables while RCS subscale scores were entered as dependent variables.

3.4.2 Paper II

For the RCS data collection in Paper II, team members needed to answer for all occupational groups included in the interprofessional rehabilitation team in one RCS question before proceeding to the next RCS question. Consequently, there were no missing values for RCS data in Papers II and III.

Patient-responses were excluded if more than two items of the NCQ-N were missing in a subscale. Furthermore, patient-responses were excluded if WHODAS 2.0 was left without a response in the one-year follow-up study. Additionally, patient respondents were excluded if not connected to the interprofessional team treating the patient, and due to missing education level.

This study used LME to investigate the research question of associations between RCS team functions and patient-reported rehabilitation benefits and continuity of care. The RCS communication and relationship scores were utilised as independent variables in these models. Dependent variables consisted of five rehabilitation benefit items from the PasOpp survey instrument and four NCQ-N subscales: personal continuity (‘knows me’), personal continuity (‘shows commitment’), team continuity and cross-boundary continuity from the one-year follow-up survey. Furthermore, the adjusting variables consisted of sex, age group at one-year follow-up, ICD-10 referral diagnosis group, origin of referral, level of education and WHODAS 2.0 global baseline score.

Four models were estimated for each of the nine dependent variables. The independent and dependent variables were included in model zero, i.e. RCS communication as the independent variable and NCQ-N Personal continuity ‘knows me’ as the dependent variable. The second model (model one) included the ICD-10 referral diagnosis as an adjustment variable. The third model (model two) included the ICD-10 referral diagnosis and WHODAS 2.0 global baseline score. The final model (model three) included the ICD-10 referral diagnosis, WHODAS 2.0 global baseline scores, sex, age-group, origin of referral and education level. All models included an Akaike information criterion (AIC) estimating the relative amount of information lost after including adjustment variables [196] (Supplementary table accompanying Paper II, appendix 2).

3.4.3 Paper III

Linear models were used to assess the research question regarding possible associations between RCS subscales (independent variables) in interprofessional rehabilitation teams and changes in patient-reported health state and level of disability (EQ-VAS and WHODAS 2.0 domain and global scores as dependent variables).

Additionally, linear models were employed to assess the research question concerning associations between patient-rated personal, team and cross-boundary continuity (NCQ-N subscales were used as independent variable) and changes in the health state and level of disability (EQ-VAS and WHODAS 2.0 domain and global scores at

follow-up were used as dependent variables). Changes in dependent variables were expressed by adjusting for WHODAS 2.0 domain and global scores and EQ-VAS baseline scores in all linear models. The LME models were employed when assessing associations between RCS subscale scores and changes in dependent variables (WHODAS 2.0 domain and global scores and EQ-VAS) due to RCS subscales being clustered at the team level. These models included a random intercept to account for possible team-level clustering.

Analyses included an interaction between ICD-10 referral diagnosis groups and WHODAS 2.0 domain and global scores/EQ-VAS. This interaction was incorporated to assess the possible different gradients present for the diagnosis groups included. All models were adjusted for sex, age group, marital status, education level and ICD-10 referral diagnosis groups.

Missing data were handled using a flexible multiple imputation (MI) method,

according to the WHODAS 2.0 manual [160]. This involved using chained predictive mean matching, creating 50 datasets [197]. The results of the 50 datasets were pooled into a final point estimate along with the standard error according to Rubin’s rules [197].