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Record W4210383874 · doi:10.1097/acm.0000000000004352

Online Medical Education: It Is Time to Listen to the Silence

2022· article· en· W4210383874 on OpenAlexaff

Bibliographic record

VenueAcademic Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsSilenceFeelingNonverbal communicationDiversity (politics)PersonalitySign (mathematics)

Abstract

fetched live from OpenAlex

To the Editor: Recently, I have seen greater recognition of the role of silence in medical education, helping reverse the negative connotations traditionally associated with silence. While silence has been interpreted to indicate a lack of knowledge, interest, or competence, it has been increasingly encouraged as an effective pedagogical tool in the classroom and the clinical setting 1: faculty make pauses during their presentations to add dramatic effect, attract students’ attention, and allow students to learn at their own pace. Clinical educators may also incorporate silence into their bedside teaching to help trainees reflect, process, interact, and ask questions. Silence is a complex, multidimensional phenomenon influenced by personality factors and sociocultural ones. For instance, introverted learners might be silent, as they save up their questions for the appropriate time, and nonverbal students tend to concentrate without feeling the urge to express their opinions. Also, there are cultural differences regarding how silence is employed and acknowledged. While silence is avoided in some cultures, it is positively viewed in others, implying respect and openness. Hence, besides the educational benefits, diversity and equality could be promoted if silence was appropriately addressed. Although silence has been discussed in face-to-face teaching, little is known about “online silence.” During the pandemic, everyone has experienced those awkward moments of silence in an online meeting. When there is no answer or comment, particularly in the absence of visible body language, it is difficult to guess whether silence is a sign of agreement, lack of interest, or a way to avoid expressing opinions. Also, whereas taking turns in speaking comes naturally in the spontaneity of free-flowing classroom conversations, taking turns online is sometimes more difficult because speakers have trouble sensing when to give up control of the conversation. Strategies enabling silence as a teaching asset in online education include listening without interrupting, offering purposeful silence, and providing active silent time. 2 Because of the discomfort around silence, teachers may evade situations with a risk of silence. Yet they could help students overcome that discomfort and contribute authentically to discussions by being explicit about silence, clarifying expectations, and maintaining a safe environment. 2 Current scholarship around silence is scarce. I therefore expect a call for further investigation to develop a deeper understanding of the notion of silence in online medical education. In-depth interviews, real-time observations, and analyses of video recordings could afford insight into how silence is perceived and addressed in online medical education and how silence is integrated into such education. Research on the role and influence of silence could equip teachers with more subtle teaching skills and help students be more engaged in learning. The findings of such research could offer practical implications to all who will probably continue teaching and learning via online platforms, even on the far side of the COVID-19 outbreak.

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)0.004
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.045
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0060.006
Open science0.0030.001
Research integrity0.0160.020
Insufficient payload (model declined to judge)0.0100.005

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 head0.032
GPT teacher head0.381
Teacher spread0.349 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations1
Published2022
Admission routes1
Has abstractyes

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