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Record W4411900168 · doi:10.25071/2291-5796.170

Resilience as Accusation: A Critical Examination of Individual Resilience Training for Burnout Mitigation

2025· article· en· W4411900168 on OpenAlexvenueno aff
Jacqueline Christianson, Bonnie Sommers-Olson, Jessica Leiberg, Dana Kaminstein

Bibliographic record

VenueWitness The Canadian Journal of Critical Nursing Discourse · 2025
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsBurnoutFalse accusationResilience (materials science)Training (meteorology)PsychologyPsychological resilienceSocial psychologyGeographyClinical psychology

Abstract

fetched live from OpenAlex

Abstract Burnout, a syndrome of work-related exhaustion and cynicism, is prevalent among nurses and is associated with workplace stressors. Resilience training programs are a prevalent method of burnout mitigation employed by healthcare institutions that aim to improve or alter how individuals respond to chronic stressors. Through the lens of General Systems Theory, we describe resilience training as a method of individualizing a systemic problem by problematizing a response to chronic stress exposure. Resilience training may furthermore serve as a mechanism which allows subversion of institutional responsibility for nurses’ well-being in the workplace. We describe several suggestions for nurses to resist being scapegoated for their responses to systemic problems. Sustainable change must include other disciplines and is likely to require multiple different avenues including individual (e.g., honoring meal breaks), institutional (e.g., increased leadership participation), legislative (e.g., mandatory staffing laws), collective (e.g., collective bargaining), and educational (e.g., emancipatory pedagogy) methods.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.043
GPT teacher head0.446
Teacher spread0.403 · 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 designTheoretical or conceptual
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

Citations0
Published2025
Admission routes1
Has abstractyes

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