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When Leaders Don’t Walk the Walk: A National Survey of Academic Nurse Leader Perceptions of Staff Burnout

2024· article· en· W4404395321 on OpenAlex
Adrienne Martinez-Hollingsworth, Dawn M. Goodolf, Nia Martin, Linda Kim, Jennifer Saylor, Jennifer Evans, Annette Hines, Jin Hyun Jun

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNursing Education Perspectives · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsLockheed Martin (Canada)
FundersHealth Resources and Services Administration
KeywordsBurnoutNursingOfficerPsychologyPerceptionPsychological resilienceNursing staffMedical educationMedicineSocial psychologyPolitical scienceClinical psychology

Abstract

fetched live from OpenAlex

AIM: This survey explored nurse leaders' impressions of burnout on college/school of nursing (CON/SON) administrative staff and leadership-facilitated strategies used to promote resilience building/mitigate burnout. BACKGROUND: Administrative staff are foundational to the success of a university's CON/SON, yet few studies have explored the impact of burnout in this group. METHOD: Cross-sectional survey distributed to associate dean and business officer attendees of the 2022 American Association of Colleges of Nursing, Business Officers of Nursing Schools meeting (summer 2022) ( n = 64). RESULTS: Most respondents lacked a burnout mitigation plan (46/64, 72%); many also lacked a personal strategy for managing their own burnout (46/64, 72%) and did not personally participate in university activities to maintain their well-being (45/64, 70%). CONCLUSION: This study highlights the impact of nurse leaders who fail to model self-care, which may limit the benefit of costly burnout mitigation activities and resources in their universities.

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.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.114
GPT teacher head0.503
Teacher spread0.388 · 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