Dataset Information

Development of an instructor guide tool: 'Three Stages of Holistic Debriefing'.

ABSTRACT: OBJECTIVE:to describe the development of an English and Brazilian Portuguese Holistic Debriefing Tool focused on nursing educator to promote a reflective learning. METHOD:a methodology study, with three phases: integrative literature review; tool development and review of a panel of nursing experts. The literature review tracked a systematic process. For the tool development were used literature review results, Lederman's Debriefing Process and Zabala's learning framework as theoretical referential to promote a reflective learning in High-Fidelity Simulation. The panel of nursing experts analysed the quality of the tool. RESULTS:literature review evidenced gaps about educator pedagogical preparation and indicated no holistic debriefing tool exists which captures formative and summative aspects of debriefing guidance to assist the educator to debrief. Debriefing tool was purposed with two pages: first page were recommended how conduct debriefing and second page is a questions guidance. The tool evaluation was undertaken for a total of three modifications for congruence and concept reader clarity. CONCLUSION:it was proposed a holistic debriefing tool focused on nursing educator. This study provides an overall picture of the process to promote a reflexive learning in High-Fidelity Simulation and to contribute to formal nursing educator training to apply best pedagogical practice.

SUBMITTER: Goes FDSN

PROVIDER: S-EPMC7000188 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Development of an instructor guide tool: 'Three Stages of Holistic Debriefing'.

Góes Fernanda Dos Santos Nogueira de FDSN Jackman Deirdre D

Revista latino-americana de enfermagem 20200203

<h4>Objective</h4>to describe the development of an English and Brazilian Portuguese Holistic Debriefing Tool focused on nursing educator to promote a reflective learning.<h4>Method</h4>a methodology study, with three phases: integrative literature review; tool development and review of a panel of nursing experts. The literature review tracked a systematic process. For the tool development were used literature review results, Lederman's Debriefing Process and Zabala's learning framework as theor ...[more]

PMID: 32022149

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Project description:BACKGROUND:Old-growth and primeval forests are passing through a natural development cycle with recurring stages of forest development. Several methods for assigning patches of different structure and size to forest development stages or phases do exist. All currently existing classification methods have in common that a priori assumptions about the characteristics of certain stand structural attributes such as deadwood amount are made. We tested the hypothesis that multivariate datasets of primeval beech forest stand structure possess an inherent, aggregated configuration of data points with individual clusters representing forest development stages. From two completely mapped primeval beech forests in Albania, seven ecologically important stand structural attributes characterizing stand density, regeneration, stem diameter variation and amount of deadwood are derived at 8216 and 9666 virtual sampling points (moving window, focal filtering). K-means clustering is used to detect clusters in the datasets (number of clusters (k) between 2 and 5). The quality of the single clustering solutions is analyzed with average silhouette width as a measure for clustering quality. In a sensitivity analysis, clustering is done with datasets of four different spatial scales of observation (200, 500, 1000 and 1500 m2, circular virtual plot area around sampling points) and with two different kernels (equal weighting of all objects within a plot vs. weighting by distance to the virtual plot center). RESULTS:The clustering solutions succeeded in detecting and mapping areas with homogeneous stand structure. The areas had extensions of more than 200 m2, but differences between clusters were very small with average silhouette widths of less than 0.28. The obtained datasets had a homogeneous configuration with only very weak trends for clustering. CONCLUSIONS:Our results imply that forest development takes place on a continuous scale and that discrimination between development stages in primeval beech forests is splitting continuous datasets at selected thresholds. For the analysis of the forest development cycle, direct quantification of relevant structural features or processes might be more appropriate than classification. If, however, the study design demands classification, our results can justify the application of conventional forest development stage classification schemes rather than clustering.

Dataset Information

Development of an instructor guide tool: 'Three Stages of Holistic Debriefing'.

Publications

Development of an instructor guide tool: 'Three Stages of Holistic Debriefing'.

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