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Record W4385294500 · doi:10.1186/s12912-023-01407-5

The relationship between moral distress, burnout, and considering leaving a hospital job during the COVID-19 pandemic: a longitudinal survey

2023· article· en· W4385294500 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBMC Nursing · 2023
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of TorontoMount Sinai Hospital
FundersCanadian Institutes of Health Research
KeywordsBurnoutDepersonalizationDistressEmotional exhaustionMedicineJob satisfactionClinical psychologyPsychologyNursingSocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Previous research suggests that moral distress contributes to burnout in nurses and other healthcare workers. We hypothesized that burnout both contributed to moral distress and was amplified by moral distress for hospital workers in the COVID-19 pandemic. This study also aimed to test if moral distress was related to considering leaving one's job. METHODS: A cohort of 213 hospital workers completed quarterly surveys at six time-points over fifteen months that included validated measures of three dimensions of professional burnout and moral distress. Moral distress was categorized as minimal, medium, or high. Analyses using linear and ordinal regression models tested the association between burnout and other variables at Time 1 (T1), moral distress at Time 3 (T3), and burnout and considering leaving one's job at Time 6 (T6). RESULTS: Moral distress was highest in nurses. Job type (nurse (co-efficient 1.99, p < .001); other healthcare professional (co-efficient 1.44, p < .001); non-professional staff with close patient contact (reference group)) and burnout-depersonalization (co-efficient 0.32, p < .001) measured at T1 accounted for an estimated 45% of the variance in moral distress at T3. Moral distress at T3 predicted burnout-depersonalization (Beta = 0.34, p < .001) and burnout-emotional exhaustion (Beta = 0.38, p < .008) at T6, and was significantly associated with considering leaving one's job or healthcare. CONCLUSION: Aspects of burnout that were associated with experiencing greater moral distress occurred both prior to and following moral distress, consistent with the hypotheses that burnout both amplifies moral distress and is increased by moral distress. This potential vicious circle, in addition to an association between moral distress and considering leaving one's job, suggests that interventions for moral distress may help mitigate a workforce that is both depleted and burdened with burnout.

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)0.014
metaresearch head score (Gemma)0.104
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.104
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0100.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.004
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 head0.415
GPT teacher head0.523
Teacher spread0.108 · 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