INTERVENCIÓN INSTITUCIONAL

Direct binary logistic regression was performed to assess the impact of a number of factors on the likelihood that respondents would intend to remain in the profession longer than 10 years at Phase Two and Phase Three. The Phase Two model contained 13

independent variables (age, sex, GPA, residency status, ESL, undergraduate employment, previous healthcare experience, stress, formal structured support program, satisfaction scores, RFP scores [Phases One and Two] and WESE scores [Phase Two]). Frequencies and collinearity statistics for predictor variables can be found in Appendix T. The variables of sex, residency status, English as a second language, and formal structured support program violated the assumption of the minimum expected cell frequency, and therefore were removed from the model. The full model containing the nine remaining predictors was not statistically significant, 2 _{(9, 𝑛 = 131) = 14.53, 𝑝 = .105, indicating that the model}

was not able to distinguish between respondents who intended to remain in the profession longer than 10 years and those who did not. The variables of age and RFP scores from Phase One and Two were the least significant variables in the model and so were removed. The full model containing the six remaining predictors was statistically

significant, 2 _{(6, 𝑛 = 131) = 14.02, 𝑝 = .029, indicating the model was able to distinguish}

between respondents who intended to remain in the profession longer than 10 years and those who did not. The model as a whole explained between 10.2% (Cox and Snell R squared) and 14.8% (Nagelkerke R squared) of the variance in successful applications to preference areas, and correctly classified 75.6% of cases. However, as shown in Table 20 only WESE score Phase Two was a statistically significant predictor in the model reporting an odds ratio of 1.07. For every point ECNs scored higher on the WESE scale, they were 1.07 times more likely to report intending to remain for more than 10 years.

The Phase Three model contained 15 independent variables (Age, Sex, GPA, Residency Status, ESL, undergraduate employment, previous healthcare experience, stress, formal structured support program, satisfaction scores, RFP scores [Phases One, Two and Three] and WESE scores [Phase Two and Three]). Frequencies and collinearity statistics for predictor variables can be found in Appendix T. The variables of sex and residency status violated the assumption of the minimum expected cell frequency, and therefore were removed from the model. The full model containing the 13 remaining

predictors was not statistically significant, 2 _{(13, 𝑛 = 129) = 20.29, 𝑝 = .088, indicating that}

profession longer than 10 years and those who did not. The variables of GPA, undergraduate employment and formal structured support program were the least significant variables in the model so were removed. The full model containing the 10 remaining predictors was statistically significant, 2 _{(10, 𝑛 = 131) = 21.51, 𝑝 = .018,}

indicating the model was able to distinguish between respondents who intended to remain in the profession longer than 10 years and those who did not. The model as a whole explained between 15.1% (Cox and Snell R squared) and 20.6% (Nagelkerke R squared) of the variance in intention to remain in the profession, and correctly classified 67.9% of cases. However, as shown in Table 20 only stress and WESE score P3 were statistically significant predictors in the model. Having a higher WESE score at Phase Three was predictive of wanting to remain in the profession for longer than 10 years; however, the strongest predictor was having stress in their personal life that was impacting on their work. Those with stress were three times more likely to remain in the profession than those without.

Table 20

Binary Logistic Regression Modelling to Predict Intention to Remain in the Profession

Variable 𝐵 𝑆𝐸 𝑂𝑅 95.0% CI Wald 𝑝

Phase Two

Grade point average -0.98 0.54 0.38 [0.13 – 1.08] 3.28 .070 Undergraduate employment 0.51 0.63 1.66 [0.49 – 5.66] 0.66 .416 Healthcare experience -0.34 0.47 0.71 [0.28 – 1.80] 0.52 .473 Stress P2 -0.24 0.49 0.78 [0.30 – 2.61] 0.24 .621 Job satisfaction P2 -0.04 0.04 0.96 [0.89 – 1.04] 1.13 .287 WESES P2 0.06 0.03 1.07 [1.01 – 1.12] 5.65 .017 Phase Three Age 0.41 0.48 1.51 [0.59 – 3.84] 0.74 .239

English as a second language 0.98 0.54 2.66 [0.93 – 7.66] 3.29 .070 Healthcare experience -0.55 0.44 0.58 [0.25 – 1.35] 1.62 .203 Stress P3 1.08 0.49 2.95 [1.14 – 7.63] 4.95 .026 Job satisfaction P3 -0.02 0.05 0.99 [0.92 – 1.05] 0.20 .653 RFP P1 -0.08 0.05 0.92 [0.84 – 1.02] 1.64 .104 RFP P2 0.03 0.06 1.03 [0.91 – 1.16] 0.19 .659 RFP P3 0.05 0.06 1.05 [0.93 – 1.18] 0.64 .425 WESE P2 0.01 0.02 1.01 [0.97 – 1.06] 0.17 .676 WESE P3 0.05 0.03 1.05 [1.00 – 1.11] 3.81 .050

Note. CI = confidence interval for odds ratio (OR); RFP = Readiness for practice scale; P2 = Phase Two;

Given the significant impact of stress and work environment, support and

encouragement (WESE) scores on intention to remain in the profession, the factors that impact on these scores are explored below.

4.16.1.1 Which factors influence stress outcomes for ECNs?

Direct binary logistic regression was performed to assess the impact of a number of factors on the likelihood that respondents would report stress at Phase Three. The model contained 12 independent variables (age, sex, GPA, residency status, ESL, undergraduate employment, previous healthcare experience, employment setting, formal structured

support program, satisfaction score, RFP score [Phase Three] and WESE scores [Phase Three]). Frequencies and collinearity statistics for predictor variables can be found in Appendix T. The variable of residency status violated the assumption of the minimum expected cell frequency, and therefore was removed from the model. The full model containing the 11 remaining predictors was not statistically significant, 2 _{(11, 𝑛 = 132) =}

16.88, 𝑝 = .112, indicating the model was not able to distinguish between respondents who reported stress in their personal life that impacted on their work and those who did not. The model as a whole explained between 12.0% (Cox and Snell R squared) and 12.6% (Nagelkerke R squared) of the variance in intention to remain in the profession, and correctly classified 69.7% of cases. Despite the model as a whole not being predictive, as seen in Table 21, RFP was a significant predictor in the model, with an inverted odds ratio of 1.10 (1 ÷ 0.91 = 1.098). This suggests that for every point ECNs scored higher on the RFP scale, they were 1.10 times less likely to report stress in their personal lives that impacted on their work.

Table 21

Binary Logistic Regression Modelling to Predict Stress

Variable 𝐵 𝑆𝐸 𝑂𝑅 95.0% CI Wald 𝑝

Age -0.30 0.47 0.75 [0.30 - 1.88] 0.39 .534

Sex 1.05 0.77 2.85 [0.63 – 12.85] 1.85 .174

Grade point average -0.32 0.45 0.73 [0.30 – 1.77] 0.49 .485 English as a second language -0.00 0.54 1.00 [0.35 – 2.85] 0.00 .996 Undergraduate employment -0.37 0.61 0.69 [0.21 – 2.30] 0.36 .550 Healthcare experience 0.64 0.47 1.89 [0.75 – 4.74] 1.84 .175 Employment setting 0.20 0.50 1.22 [0.47 – 3.24] 0.15 .695 Support program 0.94 0.87 2.57 [0.47 – 14.09] 1.18 .277 Satisfaction -0.03 0.03 0.97 [0.92 – 1.03] 1.24 .265 RFP P3 -0.10 0.05 0.91 [0.83 – 0.99] 4.67 .031 WESE P3 -0.01 0.02 0.99 [0.95 – 1.03] 0.19 .662

Note. CI = confidence interval for odds ratio (OR); RFP = Readiness for practice scale; P2 = Phase Two;

P1 = Phase One; P3 = Phase Three; WESE = Work environment, support and encouragement scale.

4.16.1.1.1 Which factors influence readiness for practice scores?

Given the impact of RFP on stress outcomes, a multiple regression analysis exploring the factors that impact on RFP scores in Phase Three was undertaken, consisting of nine dichotomous variables – Age, GPA, ESL, Previous Healthcare

Experience, Residency Status, Undergraduate Employment, Stress, Employment Setting, Relationship to Employer – and one continuous variable, Total Satisfaction Score. There were no correlations above 0.3. However, as previously identified, a significant decrease in RFP scores was identified between those with and without English as a first language in Phases Two and Three, with medium and small effect sizes respectively.

4.16.1.2 Which factors influence work environment, support and encouragement scores?

As WESE scores were predictive of intention to remain in the profession, a multiple logistic regression exploring the factors that impact on WESE scores for Phases Two and Three was undertaken, consisting of 11 variables (Age, Sex, GPA, ESL, Previous

Healthcare Experience, Residency Status, Undergraduate Employment, Stress,

Employment Setting, and Relationship to Employer, and Total Satisfaction Score). The only variable to show a correlation above 0.3 was Total Job Satisfaction in both models. Therefore the multiple logistic regression was abandoned and the correlation between these two variables was explored. A scatter plot showed a positive relationship between

WESE scores and total job satisfaction and as both were normally distributed, their relationship was explored using a Pearson product-moment correlation coefficient, which showed a strong, positive correlation between the two variables over both time points as shown in Table 22.

Table 22

Correlation between Work Environment, Support, and Encouragement Scale and Job Satisfaction 𝑛 Mean 𝑆𝐷 Pearson Correlation Sig. (2- tailed) Phase Two .70 <.001 WESE 140 90.98 11.10 Satisfaction 138 49.04 7.23 Phase Three .56 <.001 WESE 155 90.21 11.41 Satisfaction 152 46.23 8.04

Note. WESE = Work environment, support and encouragement scale; SD = Standard Deviation.

4.16.1.2.1 Which factors impact on job satisfaction scores?

A multiple regression analysis exploring the factors that impact on job satisfaction scores for Phase Three was undertaken, consisting of 10 dichotomous variables (Sex, Age, GPA, ESL, Previous Healthcare Experience, Residency Status, Undergraduate Employment, Stress, Employment Setting, Relationship to Employer) and one continuous variable (RFP score in Phase Three). There were no correlations above 0.3. However, as identified previously, a statistical difference in total job satisfaction scores was found between those who identified as having ESL and those with English as a first language.