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Record W4389203439 · doi:10.1080/23303131.2023.2287742

Evaluation of Leadership Training and Resilience Development Outcomes during the COVID-19 Pandemic

2023· article· en· W4389203439 on OpenAlexafffundabout
Rosemary Vito, Alice Schmidt-Hanbidge, Laura Brunskill, Cathy Mudge, Denisa Suteu

Bibliographic record

VenueHuman Services Organizations Management Leadership & Governance · 2023
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsThe King's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyThematic analysisPublic relationsPolitical scienceSociologyQualitative research

Abstract

fetched live from OpenAlex

Human service organizations can support leaders through training to become resilient, adaptive, and innovative during rapid complex changes. This qualitative descriptive exploratory research study reports findings on a leadership resilience training program using online evaluation surveys of 56 directors and managers in a large Canadian not-for-profit mental health/substance abuse agency. Thematic data analysis and descriptive statistics revealed positive training outcomes on four levels: satisfaction; knowledge/skills; work/team performance; and organizational resilience/sustainability. Leaders identified adaptive, resilient, and authentic leadership styles, clan/adhocracy organizational cultures, psychological safety and innovation, and organizational preparedness as important to successfully manage the pandemic and future extraordinary organizational challenges.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.399
GPT teacher head0.472
Teacher spread0.074 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2023
Admission routes3
Has abstractyes

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