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The health literacy (HL) facet Access to health information is measured in the European Health Literacy Survey (HLS-EU-Q47) by 12 items. To assess Access, we developed adapted item formulations for COVID-19 infection prevention (COVID-19-IP) and early childhood allergy prevention (ECAP) in addition to the original 12 items on General Health (GH). N = 343 (expectant) mothers of infants answered the items in an online assessment. Confirmatory structural analyses for ordinal data were adopted (WLSMV-algorithm). Women’s item ratings varied significantly across domains (η2 = .017–.552). Bi-factor models exhibited the best data fit (GH/COVID-19-IP/ECAP: CFI = .964 /.968/.977; SRMR: .062/.069 /.035): The general factor Access most strongly determined item information. Additionally, three subfactors contributed significantly (but rather weakly) to the item information in each domain. The overall score Access proved to be internally consistent (McDonald’s ωGH/COVID-19-IP/ECAP = .874/.883 /.897) and was associated with socioeconomic state (McArthur scale; rGH/COVID- 19-IP/ECAP = .218 /.210/.146). Access correlated not or only weakly with the other HL facets Understand, Appraise, and Apply. The health domains GH, COVID-19-IP, and ECAP moderated both the difficulty and the dimensional structure of the 12 Access items. This suggests that in the HLS-EU Access reflects not only the search competence but also the availability of health information.
Appropriate parental health literacy (HL) is essential to preventively maintain and promote child health. Understanding health information is assumed to be fundamental in HL models. We developed N = 67 items (multiple-choice format) based on information materials on early childhood allergy prevention (ECAP) and prevention of COVID-19 infections to assess the parental HL facet Understand. N = 343 pregnant women and mothers of infants completed the items in an online assessment. Using exploratory factor analysis for ordinal data (RML estimation) and item response models (1-pl and 2-pl model), we proved the psychometric homogeneity of the item pool. 57 items assess the latent dimension Understand according to the assumptions of the 1-pl model (weighted MNSQ < 1.2; separation reliability = .855). Person parameters of the latent trait Understand correlate specifically with subjective socioeconomic status (r = .27), school graduation (r = .46), allergy status (r = .11), and already infected with COVID-19 (r = .12). The calibrated item pool provides a psychometrically sound, constructvalid assessment of the HL facet Understand Health Information in the areas of ECAP and prevention of COVID-19 infections.
Objectives
To validate the patient-reported measure of Social Support Perceived by Patients Scale-Nurses (SuPP-N).
Design/setting
A secondary data analysis based on a cross-sectional breast cancer patient survey in 83 German hospitals. Patients were asked to give written informed consent before they were discharged. If they agreed to participate, the questionnaire was sent via mail to their home address after discharge.
Participants
Of 5583 eligible patients, 4841 consented to participate in the study and 4217 returned completed questionnaires (response rate: 75.5 %). For the data analysis n=3954 respondents were included. On average, participants were 60 years old and mostly in cancer stages I and II
Primary and secondary outcome measures
Perceived social support was assessed with a three-item patient-reported scale (SuPP-N). Convergent validity and criterion-related validity were tested using the following constructs: trust in nurses, trust in the treatment team (Wake Forest Physician Trust Scale, adapted), quality of life (European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire), processes organisation, availability of nurses.
Results
The structural equation model (SEM) assuming a one-dimensional structure of the instrument showed acceptable goodness of fit (root mean square error of approximation=0.04, Comparative Fit Index=0.96 and Tucker-Lewis Index=0.96; factor loadings ≥0.83). Hypothesis–consistent correlations with trust in nurses (beta=0.615; p<0.01) and trust in the treatment team (beta=0.264; p<0.01) proved convergent validity. Criterion-related validity was proved by its association with patients’ quality of life (beta=−0.138; p<0.01), processes organisation (beta=−0.107; p<0.01) and the availability of nurses (beta=0.654; p<0.01).
Conclusion
The results of the SEM identify potential important factors to foster social support by nurses in cancer care. In patient surveys, the SuPP-N can be used efficiently to measure patient-reported social support provided by nurses. The use of the scale can contribute to gain a better understanding of the relevance of social support provided by nurses for patients and to detect possible deficits and derive measures with the aim of improving the patient–nurse interaction.
If information on single items in the Short Form–12 health survey (SF-12) is missing, the analysis of only complete cases causes a loss of statistical power and, in case of nonrandom missing data (MD), systematic bias. This study aimed at evaluating the concordance of real patient data and data estimated by different MD imputation procedures in the items of the SF-12 assessment. For this ends, MD were examined in a sample of 1,137 orthopedic patients. Additionally, MD were simulated (a) in the subsample of orthopedic patients exhibiting no MD (n = 810; 71%) as well as (b) in a sample of 6,970 respondents representing the German general population (95.8% participants with complete data) using logistic regression modelling. Simulated MD were replaced by mean values as well as regression-, expectation-maximization- (EM-), and multiple imputation estimates. Higher age and lower education were associated with enhanced probabilities of MD. In terms of accuracy in both data sets, the EM-procedure (ICC2,1 = .33-.72) outperformed alternative estimation approaches substantially (e.g., regression imputation: ICC2,1 = .18-.48). The EM-algorithm can be recommended to estimate MD in the items of the SF-12, because it reproduces the actual patient data most accurately.
Background
The SF-8 is a short form of the SF-36 Health Survey, which is used for generic assessment of physical and mental aspects of health-related quality of life (HRQoL). Each of the 8 dimensions of the SF-36 is covered by a single item in the SF-8. The aim of the study was to examine the latent model structure of the SF-8.
Method
One-, two- and three dimensional as well as bi-factor structural models were defined and estimated adopting the ML- as well as the WLSMV-algorithm for ordinal data. The data were collected in a German general population sample (N = 2545 persons).
Results
A two- (physical and mental health) and a three-dimensional CFA structure (in addition overall health) represent the empirical data information adequately [CFI = .987/.995; SRMR = .024/.014]. If a general factor is added, the resulting bi-factor models provide a further improvement in data fit [CFI = .999/.998; SRMR = .001]. The individual items are much more highly associated with the general HRQoL factor (loadings: .698 to .908) than with the factors physical, mental, and overall health (loadings: −.206 to .566).
Conclusions
In the SF-8, each item reflects mainly general HRQoL (general factor) as well as one of the three components physical, mental, and overall health. The findings suggest in particular that the evaluation of the information of the SF-8 items can be validly supplemented by a general value HRQoL.
The COVID-19 pandemic has posed significant challenges to (expectant) mothers of infants in terms of family health protection. To meet these challenges in a health literate manner, COVID-19 protective measures must be considered important and must also be implemented appropriately in everyday life. To this end, N = 343 (expectant) mothers of infants indicated (a) how important they considered 21 COVID-19 infection prevention measures, and (b) how well they succeeded in implementing them in their daily life (20 measures). We performed data analysis using exploratory factor analysis for ordinal data and latent class analysis. One- and two-dimensional models (CFI = .960 / .978; SRMR = .053 / .039) proved to appropriately explain maternal importance ratings. The items on successfully applying COVID-19 measures in daily life can be modeled by the 5 factors hygiene measures, contact with other people, public transportation, staying at home, and checking infection status (CFI = 0.977; SRMR = .036). Six latent classes can be distinguished. Despite the largest class (39 %), classes are characterized by selective or general applicability problems. Classes reporting problems in the applicability of the measures rated them as generally less important (η = .582). Assessing and modelling importance and applicability of COVID-19 prevention measures allows for a psychometrically sound description of subjective perceptions and behaviors that are crucial for health literate practice in maternal daily life.