MétaCan
Menu
Back to cohort
Record W2922177509 · doi:10.4103/ijam.ijam_83_17

A cross-sectional online evaluation of burnout risk factors among general surgical residents in Canada

2018· article· en· W2922177509 on OpenAlexaffabout
SimonTimothy Adams, Zeeshan Rana, Rhonda Bryce, Francis Christian

Bibliographic record

VenueInternational Journal of Academic Medicine · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsUniversity of SaskatchewanRoyal University Hospital
Fundersnot available
KeywordsBurnoutMedicineCross-sectional studyCynicismOdds ratioEmotional exhaustionLogistic regressionConfidence intervalFamily medicineDisengagement theoryClinical psychologyGerontologyInternal medicine

Abstract

fetched live from OpenAlex

Background: Burnout is hallmarked by physical and psychological exhaustion, coupled with cynicism and disengagement. Evidence suggests that affected physicians not only suffer personally, but that patient safety and clinical outcomes are also negatively affected. This study aimed to identify potentially remediable risks for burnout among residents enrolled in Canadian general surgery programs.Methods: In this cross-sectional design, a questionnaire was distributed to every general surgery resident in the 15 programs consenting to participate. Questions examined the following five domains: demographics, working patterns, attitudes toward residency, life experiences, and lifestyle/outlook. Respondents' risks of burnout were assessed using the Maslach Burnout Inventory™. Univariate analysis and then multiple logistic regression were used to assess predictors.Results: A total of 114 completed questionnaires were received (22%). Of these residents, 39 (34%) met the criteria for high burnout risk. Inadequate personal/family time, a personal history of mental health or substance abuse-related issues, and moderately to poorly approachable staff/senior residents were all significantly associated with a high burnout risk (odds ratio [OR] =4.3, 95% confidence interval [CI] =1.6, 11.2, P = 0.003; OR =6.0, 95% CI = 1.6, 21.9, P = 0.007; and OR = 4.6, 95% CI = 1.7, 12.5, P = 0.003 respectively). Predicted high burnout risk probability with none of the above factors was 10%, increasing up to 40%, 75%, and 93% with one, two, or all of these risk factors present respectively.Conclusion: One-third of general surgery residents in Canada are at high burnout risk. Residency programs may have considerable influence over factors associated with this outcome to the benefit of residents, staff, and patients.The following core competencies are addressed in this article: Practice-based learning and improvement, Professionalism, Systems-based practice.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.067
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.118
GPT teacher head0.516
Teacher spread0.399 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Academic MedicineSame topicHealthcare professionals’ stress and burnoutFrench-language works237,207