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Record W3094590797 · doi:10.1503/cjs.004919

Pregnancy and parental leave among plastic surgery residents in Canada: a nationwide survey of attitudes and experiences

2020· article· en· W3094590797 on OpenAlexaffvenueabout
Haley Augustine, Syed Rizvi, Emily Dunn, J. Murphy, Helene Retrouvey, Johnny Ionut Efanov, Anna K. Steve, Becher Al‐Halabi, Ronen Avram, Sophocles H. Voineskos

Bibliographic record

VenueCanadian Journal of Surgery · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsMcGill UniversityMcMaster UniversityUniversity of TorontoUniversité de MontréalUniversity of Calgary
Fundersnot available
KeywordsMedicineWorkloadMaternity leavePregnancyProgram directorFamily medicineNursingPerceptionMedical educationSick leavePhysical therapy

Abstract

fetched live from OpenAlex

SUMMARY: Small surgical residency programs like plastic surgery can be challenging environments to accommodate parental leave. This study aimed to report the experiences, attitudes and perceived support of Canadian plastic surgery residents, recent graduates and staff surgeons with respect to pregnancy and parenting during training. Residents and staff surgeons were invited via email to participate in an online survey. The results presented here explore experiences of pregnancy and parental leave of current plastic surgery residents and staff surgeons. Residents' and staff surgeons' perceptions of program director support, policies, negative comments and the impact of parental leave on the workload of others were also explored. Although the findings suggest that there may be improvements in the support of program directors, there continues to be a negative attitude in surgical culture toward pregnancy during residency. The perceived confusion of respondents with respect to programspecific policies emphasizes the need for open conversations and standardization of parental leave.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.070
GPT teacher head0.253
Teacher spread0.183 · 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.

Study designObservational
DomainIncentives
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

Citations22
Published2020
Admission routes3
Has abstractyes

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