Health Psychology - Cardiff University

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INCREASING FLU VACCINATION UPTAKE 5 ison groups were compared using chi-square test for categorical variables and Student’s t test for continuous variables. Within group analyses involved the McNemar’s test, comparing the proportions of correct responses on knowledge of influenza symptoms and vaccine side effects at baseline and after the intervention. A series of one-way analyses of covariance (ANCOVAs; with baseline scores as covariates) was conducted on T2 data to identify any significant between group differences on the outcome measures at this time. Relationships between the hypothesized predictor and outcome measures were first analyzed by Pearson’s product moment correlations. A linear regression was then used to predict participants’ intentions, and a logistic regression was conducted to examine predictors of vaccination behavior. Mediation effects were tested using a bootstrapping procedure for multiple mediator models described by Preacher and Hayes (2008) and using the macros for both linear and dichotomous variables available from their website (http://www.quantpsy.org). The 95% bias-corrected confidence intervals of the estimates of the indirect effects (for both linear and dichotomous outcome variables) were derived with 5,000 bootstrap resamples, with indirect effects interpreted as statistically significant if the 95% confidence interval did not overlap zero. Results Measuring the Impact of the Intervention Adherence to the planning element of the intervention (i.e., full completion of all three planning elements) was high in the intervention group (95/99: 96%) and zero in the control condition. Knowledge of Influenza Symptoms and Vaccine Side Effects McNemar tests indicated significant increases in total symptom knowledge scores in both HAPA and standard leaflet groups between T1 and T2 (HAPA leaflet: from 25.25% to 56.56%, p .001; standard leaflet from 21.56% to 37.25%, p .002). Chisquare tests found no significant differences between groups at T1, but significantly higher knowledge of influenza symptoms in the intervention group at T2 (56.56% vs. 37.25%, 2 7.53, df 1, p .006). Chi-square tests revealed no statistically significant differences in the description of vaccine side effects between the two groups at T1. There were significant increases in knowledge of vaccine side effects in both HAPA leaflet and standard leaflet groups between T1 and T2 (HAPA leaflet group: from 52.0% to 86.3%, p .001; standard leaflet group from 51.5% to 84.4%, p .001). At Time 2, no significant difference in total knowledge of vaccine side effects was observed between the two groups (86.3% vs. 84.8%, 2 .08; p .77). However, more participants in the comparison group incorrectly identified breathing difficulties or swelling of the face as side effects of vaccination than those in the intervention group at T2 (17% vs. 4%, 2 9.54; p .002). Changes in HAPA Variables Following the Intervention The mean scores of all variables (risk perception, outcome expectancy, self-efficacy, and intentions) increased significantly in both groups between T1 and T2. However, participants in the intervention group showed a greater increase than the comparison group on all variables. ANCOVAs indicated significant differences between the groups at T2 on measures of perceived risk of developing influenza [F(1, 198) 26.54, p .001, p 2 .12]; perceived severity of influenza [F(1, 198) 8.44, p .004, p 2 .04]; perceived benefits from influenza vaccination [F(1, 198) 14.04, p .001, p 2 .07]; perceived self-efficacy in coping with vaccine side effects [F(1, 198) 11.80, p .001, p 2 .05]; perceived self-efficacy in arranging time and transportation to get vaccinated scores [F(1, 198) 27.0, p .001, p 2 .12]; and intention scores [F(1, 198) 33.56, p .001, p 2 .15; see Table 1]. Vaccination Behavior Eighty-nine participants of 99 in the intervention group were vaccinated compared with 86 of 102 in the comparison group. This Table 1 Mean (SD) for Subscales of Risk Perception, Outcome Expectancies, Self-Efficacy, and Intention to Obtain the Flu Vaccine at Two Time Points (n 201) Variables Total possible score Group Time 1 Mean (SD) Time 2 Mean (SD) p-value of ANCOVA Risk perception: Risk of developing influenza 35 Comparison 24.51 (3.01) 27.93 (3.63) .001 Intervention 24.09 (3.06) 30.12 (3.33) Severity of influenza 40 Comparison 28.51 (3.68) 32.98 (4.56) .004 Intervention 28.27 (3.41) 34.59 (4.14) Outcome expectancies: Perceived benefits from influenza vaccination 28 Comparison 21.25 (3.71) 23.17 (2.93) .001 Intervention 21.13 (3.81) 24.61 (2.97) Self-efficacy: Coping with vaccine side-effects 12 Comparison 9.78 (2.28) 10.84 (1.50) .001 Intervention 9.41 (1.89) 11.36 (1.18) Arranging time and transportation 16 Comparison 13.43 (2.75) 14.42 (1.96) .001 Intervention 12.74 (2.58) 15.48 (1.25) Intention 10 Comparison 7.34 (1.51) 8.38 (1.36) .001 Intervention 7.21 (1.52) 9.24 (1.20)
6 PAYAPROM, BENNETT, ALABASTER, AND TANTIPONG difference was not significant (89.90% vs. 84.31%, respectively; 2 1.39, df 1, p .23). Predicting Intentions to Obtain the Flu Vaccine In order to evaluate the ability of the HAPA model to predict the end-point of the motivational processes (intentions), change scores between T1 and T2 were created for each theoretical predictor variable (risk perception, outcome expectancy, self-efficacy), for all participants. Pearson correlations were then used to identify the association between change in each of the independent variables (and age) and changes in intentions. Changes in risk perceptions, outcome expectancies, and self-efficacy were significantly associated with changes in intentions (see Table 2). To examine whether planning was associated with changes in these mediator variables, a series of t tests was conducted comparing change scores on each variable according to whether or not participants had engaged in planning. These showed planning to be associated with significantly greater changes over time on all the variables (see Table 2). A linear regression analysis was then conducted including these variables. Together they explained 46% of the variance in intentions (adjusted R 2 .46, F 27.823, p .001). However, only planning ( 0.17, p .003), outcome expectancy change ( 0.40, p .001), and self-efficacy change in relation to arranging time and transportation, 0.31, p .001) made significant contributions to the final equation (see Table 3). Planning may have contributed to changes in intentions both directly and through its influence on other variables, and in particular self-efficacy in arranging time and transport to vaccination. To test this hypothesis, a test of mediation was conducted using the bootstrap procedure, with intentions as the dependent variable, planning as the independent variable, and self-efficacy in arranging time and transport to vaccination as the mediating variable. As predicted, the direct effect of planning on intentions remained significant ( 0.483, SE .176, t 2.75, p .006). However, a significant Table 3 Prediction of Change in Intentions (n 201) Variable B SE Beta t p Planning .52 .17 .17 2.96 .003 Change in: Perceived severity of influenza .00 .02 .01 .15 .878 Outcome expectancy .15 .02 .40 6.45 .001 Self-efficacy in coping with vaccine side-effects .06 .06 .08 1.03 .304 Self-efficacy for arranging time and transportation .16 .04 .31 3.995 .001 Note. Adj. R 2 .46, F 27.823, p .001. mediation effect was found (point estimate .280, 95% bias corrected CI 0.126–0.510). Predicting Influenza Vaccination Behavior A second (logistic) regression, involving all HAPA variables measured at T2 (perceived risk, perceived severity of influenza, outcome expectancies, self-efficacy in coping with vaccine side effects, self-efficacy for arranging time and transportation, intention, and action planning) explored the ability of the HAPA to predict behavioral outcomes. Results summarized in Table 4 showed that overall, the model had an adequate fit to the data; the model yielded a Nagelkerke R 2 of 0.52 and the Hosmer and Lemeshow was not significant ( 2 15.12, df 8, p .07). The results revealed a strong association between vaccination outcome and intention (odds ratio 3.89, p .001) and a smaller, but still significant, association with self-efficacy for arranging time and transportation (odds ratio 1.70, p .016). No independent association between planning and vaccination was found. Mediation analysis indicated that self-efficacy for arranging time and Table 2 Pearson’s Correlation Matrix of Change Scores of Social-Cognitive Variables and Between Group T-Tests of Mean Differences According to Use of Planning Variables 1 2 3 4 5 6 7 Age 1.00 .03 .11 .04 .07 .10 .13 Changes in: Perceived risk of developing influenza .03 1.00 .49 .34 .23 .16 .29 Perceived severity of influenza .11 .49 1.00 .35 .21 .27 .32 Outcome expectancies .04 .34 .35 1.00 .43 .33 .54 Self-efficacy in coping with vaccine side-effects .07 .23 .21 .43 1.00 .66 .49 Self-efficacy for arranging time and transportation .100 .16 .27 .33 .66 1.00 .54 Intention .13 .29 .32 .54 .49 .54 1.00 Mean (SD) change T1-T2 No plan Plan t p Perceived risk of developing influenza 1.06 (1.45) 2.05 (1.37) 4.98 0.001 Perceived severity of influenza 4.48 (4.9) 6.37 (4.35) 2.88 0.01 Outcome expectancies 2.03 (3.99) 3.41 (3.83) 2.48 0.014 Self-efficacy in coping with vaccine side-effects 1.10 (2.31) 2.24 (1.76) 3.896 0.001 Self-efficacy for arranging time and transportation 1.00 (2.96) 2.81 (2.45) 4.69 0.001 Intention 1.06 (1.45) 2.05 (1.37) 4.98 0.001 p .05 (two-tailed). p .01 (two-tailed).
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Health Psychology - Cardiff University

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