Evidence map›Paper›PMID 42453315›Full record

ArticleJournal of the Medical Library Association : JMLA2026

Semi-automated rubrics for evidence-based medicine assessment: a case report on grading time reduction.

Juliana Magro

Abstract read
In one paragraph

Article in Journal of the Medical Library Association : JMLA, 2026. 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

1 author.

Juliana Magrojuliana.magro@nyulangone.org, Assistant Curator & Undergraduate Medical Education Librarian, NYU Health Sciences Library, NYU Grossman School of Medicine, NYU Langone Health, New York, NY.ORCID https://orcid.org/0000-0002-9679-9501

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Librarians providing feedback on Evidence-Based Medicine (EBM) assignments face time constraints. This case report describes implementing a semi-automated rubric - defined here as a tool that automatically calculates scores from grader-selected ratings while narrative feedback is still written by the instructor - to reduce grading time for an EBM capstone assignment for first-year medical students. Case Presentation: Two cohorts of approximately 100 students completed the same EBM assignment. The 2023 cohort received manual feedback, while the 2024 cohort received feedback using the semi-automated rubric. Grading time was logged for both cohorts. The rubric, adapted from a template, automatically calculates scores based on selected criteria, streamlining the feedback process. Conclusions: The semi-automated rubric reduced grading time by 30%, from an average of seven minutes per assignment to five minutes. This simple, adaptable intervention can help reduce educator workload, improve feedback timeliness, and enhance assessment consistency. While limited by its single-grader and single institution design, this case report offers practical insights for educators seeking to improve feedback efficiency in EBM and other disciplines.

Indexed as

Educational MeasurementEvidence-Based MedicineCohort StudiesHumansLibrariansLibraries, MedicalStudents, MedicalTime FactorsAssessmentEvidence-Based MedicineMedical EducationMedical Librarianship

Identifiers

PMID42453315
PMCPMC13367300

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.