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Record W3022845971 · doi:10.1186/s12888-020-02603-2

Anxiety symptoms and burnout among Chinese medical staff of intensive care unit: the moderating effect of social support

2020· article· en· W3022845971 on OpenAlex
Hui Zhang, Zhihong Ye, Leiwen Tang, Ping Zou, Chunxue Du, Jing Shao, Xiyi Wang, Dandan Chen, Guojing Qiao, Shao Yu Mu

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

VenueBMC Psychiatry · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsNipissing University
Fundersnot available
KeywordsBurnoutSocial supportAnxietyPsychologyIntensive care unitClinical psychologyModerationTest (biology)PsychiatrySocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Social support can be a critical resource to help medical staff cope with stressful events; however, the moderating effect of social support on the relationship between burnout and anxiety symptoms has not yet been explored. METHODS: The final sample was comprised of 514 intensive care unit physicians and nurses in this cross-sectional study. Questionnaires were used to collect data. A moderated model was used to test the effect of social support. RESULTS: The moderating effect of social support was found to be significant (b = - 0.06, p = 0.04, 95%CI [- 0.12, - 0.01]). The Johnson-Neyman technique indicated that when social support scores were above 4.26 among intensive care unit medical staff, burnout was not related to anxiety symptoms. CONCLUSIONS: This is the first study to test the moderating effect of social support on the relationship between burnout and anxiety symptoms among intensive care unit staff.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.122
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.396
Teacher spread0.370 · 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