MétaCan
Menu
← Back to cohort

The anatomy of healthcare student perceptions

2016· article· en· W4389024764 on OpenAlexaff
Jared L. Dowdy, Charys M. Martin, Carol Nichols, Anna Edmondson

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsWestern University
Fundersnot available
KeywordsRubricMedical educationCurriculumHealth careNarrativePsychologyPerceptionMedicinePedagogy

Abstract

fetched live from OpenAlex

Feedback is a vital part of any healthcare career. Recognizing the importance of feedback, many healthcare professional schools are implementing reflection exercises into the curriculum. The gross anatomy lab, with its team‐oriented nature, fosters an environment conducive to developing the skills of giving and receiving feedback. The purpose of our project was to evaluate differences between how medical and allied health students perceive themselves and their peers. We hypothesized that medical and allied health students would rate themselves lower than their peers and that medical and allied health students would emphasize different themes in their narrative comments. As a part of the anatomy curriculum, students are required to complete self‐reflection and peer feedback exercises consisting of 5 rating rubric statements and sections for narrative comments on strengths and areas for improvement at the end of the course or semester. These exercises address medical knowledge, professionalism, communication, and practice‐based learning domains. We evaluated the responses of medical (n=192) and allied health (n=123) students by averaging the rating rubric responses and sorting the narrative comments into three main themes (professionalism and leadership skills, knowledge, and personal behaviors). When analyzing the rating rubric statements, there was little difference in how medical and allied health students rated their peers or themselves between the two programs. However, both medical and allied health students consistently rated themselves significantly lower than their peers. When evaluating narrative comments on strengths, students commented predominantly on professionalism and leadership traits with little difference between medical and allied health students. However, medical students emphasized knowledge more than allied health students (Peer: 16.7% vs 10.9%; Self: 10.4% vs 6.8%; p<0.01). Conversely, medical students emphasized personal behaviors less than allied health students (Peer: 22.2% vs 27.6%; Self: 19.8% vs 27.5%; p<0.01). When evaluating narrative comments on areas for improvement, medical students emphasized professionalism and leadership skills more than allied health students (Peer: 51.1% vs 42.7%; Self: 38.3% vs 27.3%; p<0.01). Again, medical students emphasized personal behaviors less than allied health students (Peer: 28.9% vs 40.0%; Self: 22.8% vs 36.4%; p<0.01). When evaluating areas of improvement, medical and allied health students emphasized knowledge and preparation similarly. Overall, there are significant differences in the emphasis placed on personal behaviors between medical and allied health students. When commenting on personal behaviors as strengths, both groups emphasized extrinsic behaviors (respect, patience, enthusiasm, and engagement). Whereas, when students commented on personal behaviors that needed improvement, both groups emphasized intrinsic behaviors (frustration, negativity, and lack of confidence). The differences found in emphasis between programs may be due to variances between department and program objectives, the timing and intensity of the anatomy courses, the degree level of the programs, and/or the varying proportions of males and females within the programs. Understanding how medical and allied health students perceive themselves and others may give us insight into methods to improve multidisciplinary communication within the healthcare setting.

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)0.006
metaresearch head score (Gemma)0.032
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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.022
GPT teacher head0.393
Teacher spread0.371 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueThe FASEB Journal→Same topicInnovations in Medical Education→French-language works237,207→