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Hierarchical cluster analysis was then applied to the units' profile data that consisted in grouping individual cases into increasingly larger clusters while ensuring both homogeneity of cases within each cluster and heterogeneity across the identified clusters.
The analytical procedures consisted of first generating unit profiles based on qualitative and quantitative data collected at the unit level, then applying hierarchical cluster analysis to the units' profile data.
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Table 2 presents a matrix of the units' profiles for the two practice environment factors and the resulting classification of the units for this dimension.
Table 3 presents a matrix of the units' profiles for the two indicators related to staffing and the resulting classification of the units for this dimension.
Table 2 presents a matrix of the units' profiles for the two actual scope of practice factors and the resulting classification of the units for this dimension.
Table 3 presents a matrix of the units' profiles for the five indicators related to capacity for innovation and the resulting classification of the units for this dimension.
The objective at this step was to describe the units' profiles for each component of the framework and rank them based on their profiles.
Our explanatory variables are listed in Tables 1 and 2. We selected these variables based on those available to us in the unit profile and staff surveys.
Only the unit profile, the routine clinical practice with respect to PN and awareness and implementation of guidelines were analysed for this report.
From the unit profile survey we obtained the following two variables (each assigned as unit-level measurement in our models): (1) average number of beds occupied and (2) average length of patient stay (in days).
We were able to contact (and thus invite to participate) 80% of all eligible participants; the number of eligible participants was determined by the study research nurse in consultation with the unit managers while completing the unit profile survey.
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