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Record W4322708793 · doi:10.9778/cmajo.20220191

Perceived workplace support and mental health, well-being and burnout among health care professionals during the COVID-19 pandemic: a cohort analysis

2023· article· en· W4322708793 on OpenAlexvenueno aff
Imrana Siddiqui, Jaya Gupta, George Collett, Iris McIntosh, Christina Komodromos, Thomas Godec, Sher May Ng, Carmela Maniero, Sotiris Antoniou, Rehan Ullah Khan, Vikas Kapil, Mohammed Y Khanji, Ajay Gupta

Bibliographic record

VenueCMAJ Open · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsnot available
FundersBarts Health NHS TrustBarts Charity
KeywordsDepersonalizationMental healthEmotional exhaustionBurnoutAnxietyClinical psychologyMedicineCohortLogistic regressionThematic analysisPsychiatryPatient Health QuestionnaireSocial supportPsychologyHospital Anxiety and Depression ScaleQualitative researchDepressive symptoms

Abstract

fetched live from OpenAlex

<h3>Background:</h3> Little is known about the relationship between workplace support and mental health and burnout among health care professionals (HCPs) during the COVID-19 pandemic. In this cohort study, we sought to evaluate the association between perceived level of (and changes to) workplace support and mental health and burnout among HCPs, and to identify what constitutes perceived effective workplace support. <h3>Methods:</h3> Online surveys at baseline (July–September 2020) and follow-up 4 months later assessed the presence of generalized anxiety disorder (using the 7-item Generalized Anxiety Disorder scale [GAD-7]), clinical insomnia, major depressive disorder (using the 9-item Patient Health Questionnaire), burnout (emotional exhaustion and depersonalization) and mental well-being (using the Short Warwick-Edinburgh Mental Wellbeing Score). Both surveys assessed self-reported level of workplace support (single-item Likert scale). For baseline and follow-up, independently, we developed separate logistic regression models to evaluate the association of the level of workplace support (tricohotomized as unsupported, neither supported nor unsupported and supported) with mental health and burnout. We also developed linear regression models to evaluate the association between the change in perceived level of workplace support and the change in mental health scores from baseline and follow-up. We used thematic analyses on free-text entries of the baseline survey to evaluate what constitutes effective support. <h3>Results:</h3> At baseline (<i>n</i> = 1422) and follow-up (<i>n</i> = 681), HCPs who felt supported had reduced risk of anxiety, depression, clinical insomnia, emotional exhaustion and depersonalization, compared with those who felt unsupported. Among those who responded to both surveys (<i>n</i> = 681), improved perceived level of workplace support over time was associated with significantly improved scores on measures of anxiety (adjusted β −0.13, 95% confidence interval [CI] −0.25 to −0.01), depression (adjusted β −0.17, 95% CI −0.29 to −0.04) and mental well-being (adjusted β 0.19, 95% CI 0.10 to 0.29), independent of baseline level of support. We identified 5 themes constituting effective workplace support, namely concern or understanding for welfare, information, tangible qualities of the workplace, leadership and peer support. <h3>Interpretation:</h3> We found a significant association between perceived level of (and changes in) workplace support and mental health and burnout of HCPs, and identified potential themes that constitute perceived workplace support. Collectively, these findings can inform changes in guidance and national policies to improve mental health and burnout among HCPs. <b>Trial registration:</b>ClinicalTrials.gov, no. NCT04433260

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0090.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.061
GPT teacher head0.453
Teacher spread0.392 · 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 designObservational
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

Citations14
Published2023
Admission routes1
Has abstractyes

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