Evidence map›Paper›PMID 36591429›Full record

ArticleJournal of graduate medical education2022

A Model for Work Intensity in a Pediatric Training Program.

Janani Sundaresan, Sebastian Ty Ferrell, Jonathan D Hron

Abstract read
In one paragraph

Article in Journal of graduate medical education, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Janani Sundaresanis a PGY-3 Resident, Boston Combined Residency Program, Department of Pediatrics Boston Children's Hospital and Boston Medical Center.ORCID https://orcid.org/0000-0001-8803-0340
Sebastian Ty Ferrellis a former Graduate Medical Education Data Analyst, Department of Medical Education, Boston Children's Hospital.
Jonathan D Hronis a Pediatric Hospitalist, Division of General Pediatrics, Boston Children's Hospital, and Assistant Professor of Pediatrics, Harvard Medical School.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The Accreditation Council for Graduate Medical Education (ACGME) requires residency programs to monitor scheduling, work intensity, and work compression. Objective: We aimed to create a model for assessing intern work intensity by examining patient and clinical factors in our electronic health systems using multiple linear regression. Methods: We identified measurable factors that may contribute to resident work intensity within our electronic health systems. In the spring of 2021, we surveyed interns on pediatric hospital medicine rotations each weekday over 5 blocks to rank their daily work intensity on a scale from -100 (bored) to +100 (exasperated). We queried our electronic systems to identify patient care activities completed by study participants on days they were surveyed. We used multiple linear regression to identify factors that correlate with subjective scores of work intensity. Results: Nineteen unique interns provided 102 survey responses (28.3% response rate) during the study period. The mean work intensity score was 9.82 (SD=44.27). We identified 19 candidate variables for the regression model. The most significantly associated variables from our univariate regression model were text messages (β=0.432, Conclusions: Our study demonstrates that data extracted from electronic systems can be used to estimate resident work intensity.

Indexed as

Internship and ResidencyMedicineAccreditationChildEducation, Medical, GraduateHumansWorkloadWork Schedule Tolerance

Identifiers

PMID36591429
PMCPMC9765907

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.