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Record W4399871411 · doi:10.3390/educsci14060667

School Leader Well-Being: Perceptions of Canada’s Outstanding Principals

2024· article· en· W4399871411 on OpenAlexaffabout
Benjamin Kutsyuruba, Nadia Arghash, Maha Al Makhamreh

Bibliographic record

VenueEducation Sciences · 2024
Typearticle
Languageen
FieldPsychology
TopicGrit, Self-Efficacy, and Motivation
Canadian institutionsQueen's University
Fundersnot available
KeywordsPerceptionPsychologyMathematics educationPedagogySocial psychologySociology

Abstract

fetched live from OpenAlex

The decrease in well-being of school leaders has become an area of concern among scholars and practitioners around the world. Globally, increasing social, political, economic, educational, and professional demands faced by school administrators have led to an unmanageable workload, stress, burnout, and a lack of work–life balance. However, some principals thrive amidst challenges and are recognized by various national and international awards as outstanding school leaders. Examining the challenges that award-winning school principals face can both prevent conditions that decrease their well-being and help understand coping strategies and support systems necessary for successful school leadership. Our study examined the sense of flourishing among the national award-winning principals in the Canada’s Outstanding Principals (COP) program that recognizes outstanding contributions of principals in publicly funded schools. In this article, we describe participants’ perceptions regarding the significance of principal well-being, barriers and challenges to their well-being, coping strategies to promote and sustain their well-being, and necessary policy and school system supports for enhanced principal well-being.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.041
GPT teacher head0.379
Teacher spread0.338 · 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

Citations6
Published2024
Admission routes2
Has abstractyes

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