Nest-site characteristics of prairie shorebirds

1990 ◽  
Vol 68 (2) ◽  
pp. 297-302 ◽  
Author(s):  
Mark A. Colwell ◽  
Lewis W. Oring
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Nest Site ◽  
Mountain Lake ◽  
Vegetation Density ◽  
South Central ◽  
Site Characteristics ◽  
Habitat Gradient ◽  
Components Analysis ◽  
Wilson’S Phalarope

Nest-site characteristics of eight shorebird species were studied during the period 1982–1984 at Last Mountain Lake National Wildlife Area in south central Saskatchewan. Plant species composition at nests varied significantly among species and was correlated with distance to bare ground and aquatic habitat. Flora at Wilson's phalarope (Phalaropus tricolor) nests was correlated with clutch initiation date, reflecting a seasonal shift in nesting habitat. Species differed significantly in cover types at nests. For American avocet (Recurvirostra americana), killdeer (Charadrius vociferus), and Wilson's phalarope, cover types differed between nests and random sites; cover types also differed between nest cups and the immediate surrounding habitat for killdeer and Wilson's phalarope. Interspecific differences in nest-site vegetation physiognomy were portrayed by the first two factors of a principal components analysis, which accounted for 73 and 22% of variance in the data, respectively. Principal components analysis assembled species on a habitat gradient that varied according to cover type, and vegetation density and heterogeneity.

scholarly journals A principal components analysis of factors associated with successful implementation of an LVAD decision support tool

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kristin M. Kostick ◽  
Meredith Trejo ◽  
Arvind Bhimaraj ◽  
Andrew Civitello ◽  
Jonathan Grinstein ◽  
...  
Keyword(s):  
Decision Support ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Decision Aid ◽  
Left Ventricular ◽  
Successful Implementation ◽  
Implementation Success ◽  
Site Characteristics ◽  
Multi Level ◽  
Components Analysis

Abstract Background A central goal among researchers and policy makers seeking to implement clinical interventions is to identify key facilitators and barriers that contribute to implementation success. Despite calls from a number of scholars, empirical insights into the complex structural and cultural predictors of why decision aids (DAs) become routinely embedded in health care settings remains limited and highly variable across implementation contexts. Methods We examined associations between “reach”, a widely used indicator (from the RE-AIM model) of implementation success, and multi-level site characteristics of nine LVAD clinics engaged over 18 months in implementation and dissemination of a decision aid for left ventricular assist device (LVAD) treatment. Based on data collected from nurse coordinators, we explored factors at the level of the organization (e.g. patient volume), patient population (e.g. health literacy; average sickness level), clinician characteristics (e.g. attitudes towards decision aid; readiness for change) and process (how the aid was administered). We generated descriptive statistics for each site and calculated zero-order correlations (Pearson’s r) between all multi-level site variables including cumulative reach at 12 months and 18 months for all sites. We used principal components analysis (PCA) to examine any latent factors governing relationships between and among all site characteristics, including reach. Results We observed strongest inclines in reach of our decision aid across the first year, with uptake fluctuating over the second year. Average reach across sites was 63% (s.d. = 19.56) at 12 months and 66% (s.d. = 19.39) at 18 months. Our PCA revealed that site characteristics positively associated with reach on two distinct dimensions, including a first dimension reflecting greater organizational infrastructure and standardization (characteristic of larger, more established clinics) and a second dimension reflecting positive attitudinal orientations, specifically, openness and capacity to give and receive decision support among coordinators and patients. Conclusions Successful implementation plans should incorporate specific efforts to promote supportive and mutually informative interactions between clinical staff members and to institute systematic and standardized protocols to enhance the availability, convenience and salience of intervention tool in routine practice. Further research is needed to understand whether “core predictors” of success vary across different intervention types.

Multiple Pattern Analysis of a Woodland in South Central Queensland. I. The Original Trees and Shrubs

1972 ◽  
Vol 20 (1) ◽  
pp. 105 ◽  
Author(s):  
J Walker ◽  
I Noy-Meir ◽  
DJ Anderson ◽  
RM Moore
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Pattern Analysis ◽  
Characteristic Root ◽  
Block Size ◽  
Single Species ◽  
South Central ◽  
Single Scale ◽  
Components Analysis ◽  
Block Sizes

Multivariate analyses of vegetation data are normally restricted to a single scale of sampling, but since the pattern of species populations may vary over a range of scales, restriction to a single scale can result in a loss of potentially useful information. It is possible to examine spatial variation for a single species or pairs of species by block size variance (or covariance) analysis, but this is a somewhat cumbersome procedure when more than a few species are involved. A method which combines block size variance analysis with a multivariate (principal components) analysis is described and applied to a woodland community in south central Queensland. Contiguous site data, recorded as density scores for all tree and shrub species along a transect 512 m by 20 m, were grouped into successively larger blocks. Variance covariance matrices at each block size were calculated and added to form a combined covariance matrix. This was subjected to a principal components analysis to obtain species and sites coordinates. Each characteristic root was subsequently partitioned into contributions from the various block sizes, and the partitioned roots plotted against block size as in conventional pattern analysis. The first two components represented macro-variation in the vegetation of the transect (at scales of 120-250 m) and separated three macrocommunities which were associated with soil types. Two subsequent components expressed compositional differences at smaller scales (30-60 m) within these macrocommunities.

scholarly journals A Principal Components Analysis of Factors Associated with Successful Implementation of an LVAD Decision Support Tool

2020 ◽  
Author(s):  
Kristin M. Kostick ◽  
Meredith Trejo ◽  
Arvind Bhimaraj ◽  
Andrew Civitello ◽  
Jonathan Grinstein ◽  
...  
Keyword(s):  
Decision Support ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Decision Aid ◽  
Left Ventricular ◽  
Successful Implementation ◽  
Implementation Success ◽  
Site Characteristics ◽  
Multi Level ◽  
Components Analysis

Abstract BackgroundA central goal among researchers and policy makers seeking to implement clinical interventions is to identify key facilitators and barriers that contribute to implementation success. Despite calls from a number of scholars, empirical insights into the complex structural and cultural predictors of why DAs become routinely embedded in health care settings remains limited and highly variable across implementation contexts.MethodsWe examined associations between “reach”, a widely used indicator (from the RE-AIM model) of implementation success, and multi-level site characteristics of nine LVAD clinics engaged over 18 months in implementation and dissemination of a decision aid for left-ventricular assist device (LVAD) treatment. Based on data collected from nurse coordinators, we explored factors at the level of the organization (e.g. patient volume), patient population (e.g. health literacy; average sickness level), clinician characteristics (e.g. attitudes towards decision aid; readiness for change) and process (how the aid was administered). We generated descriptive statistics for each site and calculated zero-order correlations (Pearson’s r) between all multi-level site variables including cumulative reach at 12 months and 18 months for all sites. We used principal components analysis (PCA) to examine any latent factors governing relationships between and among all site characteristics, including reach.ResultsWe observed strongest inclines in reach of our decision aid across the first year, with uptake fluctuating over the second year. Average reach across sites was 63% (s.d.=19.56) at 12 months and 66% (s.d.=19.39) at 18 months. Our (PCA) revealed that site characteristics positively associated with reach on two distinct dimensions, including a first dimension reflecting greater organizational infrastructure and standardization (characteristic of larger, more established clinics) and a second dimension reflecting positive attitudinal orientations, specifically, openness and capacity to give and receive decision support among coordinators and patients.ConclusionsSuccessful implementation plans should incorporate specific efforts to promote supportive and mutually informative interactions between clinical staff members and to institute systematic and standardized protocols to enhance the availability, convenience and salience of intervention tool in routine practice. Further research is needed to understand whether “core predictors” of success vary across different intervention types.

scholarly journals A Principal Components Analysis of Factors Associated with Successful Implementation of an LVAD Decision Support Tool

2020 ◽  
Author(s):  
Kristin M. Kostick ◽  
Meredith Trejo ◽  
Arvind Bhimaraj ◽  
Andrew Civitello ◽  
Jonathan Grinstein ◽  
...  
Keyword(s):  
Decision Support ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Decision Aid ◽  
Left Ventricular ◽  
Successful Implementation ◽  
Implementation Success ◽  
Site Characteristics ◽  
Multi Level ◽  
Components Analysis

Abstract Background: A central goal among researchers and policy makers seeking to implement clinical interventions is to identify key facilitators and barriers that contribute to implementation success. Despite calls from a number of scholars, empirical insights into the complex structural and cultural predictors of why DAs become routinely embedded in health care settings remains limited and highly variable across implementation contexts.Methods: We examined associations between “reach”, a widely used indicator (from the RE-AIM model) of implementation success, and multi-level site characteristics of nine LVAD clinics engaged over 18 months in implementation and dissemination of a decision aid for left-ventricular assist device (LVAD) treatment. Based on data collected from nurse coordinators, we explored factors at the level of the organization (e.g. patient volume), patient population (e.g. health literacy; average sickness level), clinician characteristics (e.g. attitudes towards decision aid; readiness for change) and process (how the aid was administered). We generated descriptive statistics for each site and calculated zero-order correlations (Pearson’s r) between all multi-level site variables including cumulative reach at 12 months and 18 months for all sites. We used principal components analysis (PCA) to examine any latent factors governing relationships between and among all site characteristics, including reach.Results: We observed strongest inclines in reach of our decision aid across the first year, with uptake fluctuating over the second year. Average reach across sites was 63% (s.d.=19.56) at 12 months and 66% (s.d.=19.39) at 18 months. Our (PCA) revealed that site characteristics positively associated with reach on two distinct dimensions, including a first dimension reflecting greater organizational infrastructure and standardization (characteristic of larger, more established clinics) and a second dimension reflecting positive attitudinal orientations, specifically, openness and capacity to give and receive decision support among coordinators and patients.Conclusions: Successful implementation plans should incorporate specific efforts to promote supportive and mutually informative interactions between clinical staff members and to institute systematic and standardized protocols to enhance the availability, convenience and salience of intervention tool in routine practice. Further research is needed to understand whether “core predictors” of success vary across different intervention types.

Diagnoses Generated by Numerical Taxonomic Methods Applied to Standard Blood Variables

1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton
Keyword(s):  
Liver Cirrhosis ◽  
Renal Disease ◽  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Chronic Renal Disease ◽  
Function Analysis ◽  
Infectious Hepatitis ◽  
Components Analysis

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.

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