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Record W4393933371 · doi:10.1080/13603124.2024.2334260

Flourishing among Canada’s outstanding principal award recipients: the critical role of resilience

2024· article· en· W4393933371 on OpenAlexaffabout
Benjamin Kutsyuruba, Nadia Arghash, Jodi Basch

Bibliographic record

VenueInternational Journal of Leadership in Education · 2024
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsQueen's University
Fundersnot available
KeywordsFlourishingResilience (materials science)Principal (computer security)PsychologySociologyPolitical scienceEnvironmental ethicsGerontologyMedicineSocial psychologyPhilosophyComputer scienceComputer security

Abstract

fetched live from OpenAlex

School leaders in Canada face increasing social, political, economic, educational, and professional demands, which often lead to increased workload, stress, burnout, decreased well-being, and lack of work-life balance. Research demonstrates that school principals with high levels of resilience are better at coping with stress and crisis, are generally more effective as leaders, are more connected to schools and districts, and have lower levels of compassion fatigue. Examining the challenges that school principals face can both prevent conditions that decrease their well-being and help understand coping strategies and resilience-building approaches necessary for successful school leadership. Drawing from the study of flourishing among the national award-winning principals in the Canada’s Outstanding Principals (COP) program, in this article we describe the participants’ perceptions regarding their resilience as school leaders, the conditions that discouraged school principals in their role, and the approaches that allowed them to develop their personal resilience. The article offers recommendations on how school administrators can overcome the challenges and flourish by fostering resilience and growing a resilient mind-set.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

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

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 head0.066
GPT teacher head0.420
Teacher spread0.354 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2024
Admission routes2
Has abstractyes

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