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Changes in Dental Student Empathy During Training

2009· article· en· W2169380980 on OpenAlexaffabout
Carilynne Yarascavitch, Glenn Regehr, Brian Hodges, Daniel A. Haas

Bibliographic record

VenueJournal of Dental Education · 2009
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsThe Wilson CentreUniversity of Toronto
Fundersnot available
KeywordsEmpathyPsychologyContext (archaeology)EmotiveClinical psychologyCognitionRepeated measures designSocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

Because empathic patient interactions by dentists are associated with improved patient outcomes, self-reported declines in empathy during dental student training are a concern. This study examined differences in empathy in 178 dental students at the University of Toronto and the University of Western Ontario from years one through four using an anonymous self-report web-based survey in a cross-sectional design. To localize the effects of training on empathy, an instrument that separately evaluated emotive (Emo) and cognitive (Cog) types of empathy in both personal (Per) and professional (Pro) contexts was developed, using items modified from previously validated scales and resulting in an empathy scale with four thirteen-item subscales (Per-Emo, Per-Cog, Pro-Emo, Pro-Cog). The response rate was 36.5 percent, and all subscales showed good reliability and validity. A 2x2x4 mixed design ANOVA tested differences in mean scores among the four subscales across the four years of training. Following a significant three-way interaction, subanalyses demonstrated no significant effects in the Per-context, but a significant year by empathy-type interaction in the Pro-context. Post hoc analyses of Pro measures indicated year three emotive empathy scores were significantly lower than earlier years, whereas years three and four cognitive empathy scores were significantly higher. This isolated decrease in Pro-Emo empathy with an increase in Pro-Cog empathy is consistent with the development of "professional empathy," described elsewhere as detached concern.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.526
Threshold uncertainty score0.307

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.358
Teacher spread0.336 · 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 teacher head, 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

Citations68
Published2009
Admission routes2
Has abstractyes

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