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Record W3200606237 · doi:10.1080/13603124.2021.1926545

Moral distress among school leaders: an Alberta, Canada study with global implications

2021· article· en· W3200606237 on OpenAlexaffabout
Bonnie Stelmach, Lee Smith, Barbara J. Virley O'Connor

Bibliographic record

VenueInternational Journal of Leadership in Education · 2021
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsScholarshipDistressMoral disengagementSocial psychologyPsychologySociologyFace (sociological concept)Political scienceSocial scienceLaw

Abstract

fetched live from OpenAlex

Moral distress is experienced when one knows the right thing to do, but cannot do it because of institutional or external constraints. This study aimed to understand the extent to which moral distress affects a school leader’s role, and the key sources of moral distress. Using a web-based survey (n = 954) and focus groups including school leaders, we found that the increasing complexity of classrooms places competing demands upon school leaders. Moral distress emerged from expectations from school district leaders and parents. This study introduces moral distress to educational research, and provides a conceptual lens for describing the moral dimension of challenges that school leaders face. Future scholarship is necessary to understand the impact of moral distress on school leaders as they strive to adapt to increasing demands from both their districts and their school communities.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.382

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.006
Science and technology studies0.0180.003
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.238
GPT teacher head0.509
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations10
Published2021
Admission routes2
Has abstractyes

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