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Record W2993313054 · doi:10.5455/jpma.285942

Work-life balance amongst residents in surgical and non-surgical specialties in a tertiary care hospital in Karachi

2019· article· en· W2993313054 on OpenAlexaboutno aff
Saad Akhtar Khan, Muhammad Waqas, Mubbashira Siddiqui, Badar Uddin Ujjan, Marium Khan, Muhammad Ehsan Bari, Muhammad Azeem

Bibliographic record

VenueJournal of the Pakistan Medical Association · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTertiary careBalance (ability)Family medicineGeneral surgeryPhysical therapy

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess work-life balance among medical residents at a tertiary hospital. METHODS: The cross-sectional study was conducted from September to December 2016 at a private-sector tertiary care hospital in Karachi, and comprised medical residents working at the facility. A standardised, self-administered questionnaire was developed on the basis of Canadian Mental Health quiz and a study in literature. The questions aimed at assessing satisfaction with work as well as emotional and personal life of residents in various medical and surgical specialties. SPSS 20 was used for data analysis. RESULTS: Of the 275 residents, 129(46.9%) were males and 146(53.1%) were females. The overall mean age was 28.19±2.194 years. Of the total, 13(4.7%) participants thought they had work-life balance; 165(60%) felt their job had negatively affected their private lives; 118(42.9%) felt worn out; 109(39.6%) expressed moderate dissatisfaction with work-related factors; 119(43.3%) were dissatisfied with life outside work; and 93(33.8%) were dissatisfied their health. CONCLUSIONS: There was minimal work-life balance among the residents.

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)0.002
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 score0.010
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.005
GPT teacher head0.272
Teacher spread0.267 · 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.

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

Citations4
Published2019
Admission routes1
Has abstractyes

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