Evidence map›Paper›PMID 38882241›Full record

ArticleAEM education and training2024

Making goals count: A theory-informed approach to on-shift learning goals.

Max Griffith, Ivan Zvonar, Alexander Garrett, Naeem Bayaa

Abstract read
In one paragraph

Article in AEM education and training, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

4 authors.

Max GriffithDepartment of Emergency Medicine University of Washington Seattle Washington USA.ORCID https://orcid.org/0000-0002-7596-5785
Ivan ZvonarDepartment of Emergency Medicine University of Washington Seattle Washington USA.ORCID https://orcid.org/0009-0007-1915-7461
Alexander GarrettDepartment of Emergency Medicine University of Washington Seattle Washington USA.
Naeem BayaaDepartment of Emergency Medicine University of Washington Seattle Washington USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Supervisors often ask emergency medicine trainees for their learning goals at the start of a clinical shift, though they may do so without considering the reasons for this practice. Recognizing the underlying rationale for voicing on-shift learning goals and proactively considering solutions for some of the associated challenges can help learners and supervisors employ this practice to its full potential. Goal articulation is rooted in educational principles such as self-regulated learning, targeted performance feedback, and collaborative relationships between learner and supervisor. Despite the potential for on-shift learning goals to augment learning, there are numerous barriers that make it challenging for learners and supervisors alike to create or follow up on meaningful goals. Learner-related challenges include uncertainty about how to develop goals within an unpredictable clinical environment and creating goals too narrow or broad in scope. Supervisor-related challenges include difficulties integrating direct observation into the clinical workflow and a desire to avoid negative feedback. The learning environment also presents inherent challenges, such as lack of longitudinal supervisor-learner relationships, time constraints, space limitations, and incentives for learners to conceal their knowledge gaps. The authors discuss these challenges to effective on-shift learning goals and propose solutions that target the learner's approach, the supervisor's approach, and the learning environment itself.

Identifiers

PMID38882241
PMCPMC11178521

What OpenQuestion holds

Textmetadata
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.