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Record W2143151238 · doi:10.1002/ca.1037

Dissection: A positive experience

2001· article· en· W2143151238 on OpenAlexaboutno aff
M.A. Mc Garvey, Thomas B. Farrell, Ronán Conroy, Shivanthi Kandiah, W. S. Monkhouse

Bibliographic record

VenueClinical Anatomy · 2001
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineWorkloadDissection (medical)Stress (linguistics)Surgery

Abstract

fetched live from OpenAlex

First-year medical students were surveyed by questionnaire to assess levels of stress and physical symptoms resulting from their experience of the anatomy room. There was a 100% response rate from the 188 students. Most students (95%) found the prospect of their first visit to the anatomy room exciting. A small number initially experienced physical symptoms, but these had improved significantly 10 weeks later. Most students suffered very little or no stress (80%) on their first visit with only 2% of respondents rating their stress levels as high. Ten weeks later, 87% experienced little or no stress with only 1% stating that they had high stress levels. The anatomy room was rated to be less stressful than workload and assessments. Students reported that the anatomy room provoked thoughts of mortality, and 27% suggested that there should be more preparation before the first visit to the anatomy room. Our findings support previous studies suggesting that American/Canadian students in particular find anatomy stressful. However, the wisdom of interpreting adverse reactions as symptomatic of post-traumatic stress disorder is questioned. This study shows the anatomy room to be a positive learning experience for the students of the Royal College of Surgeons in Ireland.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0000.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.084
GPT teacher head0.463
Teacher spread0.379 · 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 designObservational
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

Citations108
Published2001
Admission routes1
Has abstractyes

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