Spss software v24

An anonymous survey with 26 item electronic was distributed to members of the Canadian Ophthalmological Society (COS) including the Canadian Glaucoma Society. The questionnaire was validated for content, clarity, and comprehensiveness by three glaucoma specialists. Treatment indications, algorithm and techniques, and questions specific to SLT were investigated. Respondents who did not use LTP in their practice were excluded. The initial invitation, with one follow-up reminder e-mail 3 weeks later, was sent in the Spring of 2018. The survey data included multiple-choice and free-text questions. Data were entered in a spreadsheet. Descriptive statistics was performed. The frequencies and relative frequencies are reported for each question. Chi-square test with continuity correction or Fisher's exact test were used for group comparison. A p value of 0.05 was considered for statistical significance, and SPSS software v.24 (Armonk, NY: IBM Corp) was used for data analysis. For comparisons, variable for length of practice was dichotomized to less than 15 years and greater than or equal to 15 years of practice.

Cronbach’s alpha was used to assess the internal reliability of the scales (values above 0.70 are considered acceptable, 0.80 good and 0.90 very good).41 (link) Feasibility of 3GT for HCWs was assessed through attrition (uptake) and participant evaluations. Independent samples t-tests examined whether baseline levels of the well-being metrics predicted dropping out of the study at the 1, 6 and 12-month follow-ups. Paired samples t-tests were used to test for improvement in the well-being variables across the baseline and 1, 6 and 12-month follow-ups. These analyses used listwise deletion for missing data. Participants who were lost to follow-up were still included in any analysis for which they had provided data. For all analyses, p values less than 0.05 were considered significant and all tests were two tailed. A power analysis indicated that our sample size provided high power (1-β)>0.9 to detect relatively small effect sizes (Cohen’s d=0.3). Cohen’s d effect sizes for correlated samples were computed using the formula d=M_diff/SD_pooled. Generally, effect sizes around 0.2 are considered small, 0.5 are considered medium, 0.8 large, 1.2 very large and 2.0 huge.42 Analyses were performed using IBM SPSS software V.24.