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Record W2109031423 · doi:10.5770/cgj.17.74

Assessing the Impact of a Geriatric Clinical Skills Day on Medical Students’ Attitudes Toward Geriatrics

2013· article· en· W2109031423 on OpenAlexaffvenueabout
Andalib F. Haque, Daniel Soong, Camilla L. Wong

Bibliographic record

VenueCanadian Geriatrics Journal · 2013
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsSt. Michael's HospitalUniversity of Toronto
Fundersnot available
KeywordsGeriatricsMedicineSpecialtyTest (biology)Family medicinePopulationMedical educationGerontologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: The aging population requires an improvement in physicians' attitudes, knowledge, and skills, regardless of their specialty. This study aimed to identify attitude changes of University of Toronto pre-clerkship medical students towards geriatrics after participation in a Geriatric Clinical Skills Day (GCSD). METHODS: This was a before and after study. The GCSD consisted of one large and four small interactive, inter-professional geriatric medicine workshops facilitated by various health professionals. A questionnaire, including the validated UCLA Geriatrics Attitudes Scale, was administered to participating pre-clerkship medical students before and after the GCSD. A one-sample t-test and signed rank parametric test were used to determine attitude changes. RESULTS: 42.1% indicated an interest in Geriatric Medicine, 26.3% in Geriatric Psychiatry, and 63.2% in working with elderly patients. Both pre- and post-mean scores were greater than 3 (neutral), indicating a positive attitude before and after the intervention (p < .001). There was no significant difference in the change in mean total scores (signed rank test p ≥ .12, Student's t-test p > .11). CONCLUSIONS: The GCSD did not alter pre-clerkship students' attitudes towards geriatrics. This study adds to geriatric medical education research and warrants further investigation in a larger, multi-centred trial.

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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0020.000
Research integrity0.0010.002
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 head0.073
GPT teacher head0.472
Teacher spread0.399 · 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.

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

Citations12
Published2013
Admission routes3
Has abstractyes

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