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Insights from Students Following an Educational Rotation Through Dental Geriatrics

2005· article· en· W2298167812 on OpenAlexaff
Michael I. MacEntee, Matana Kettratad‐Pruksapong, Chris Wyatt

Bibliographic record

VenueJournal of Dental Education · 2005
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMultidisciplinary approachGeriatricsWitnessContext (archaeology)Medical educationPsychologyGeriatric dentistryHealth careMedicineOral healthQualitative researchNursingFamily medicineSociologyPsychiatry

Abstract

fetched live from OpenAlex

Little is known about how dental students respond to dental geriatrics. This article describes a qualitative analysis of reflective journals submitted over two years by ninety-two senior students who participated in a brief clinical rotation in long-term care facilities. We used an inductive interpretive approach to analyze the journals. Eight themes emerged from the analysis: 1) complexity of the institutional environment; 2) heterogeneity of the resident population; 3) multidisciplinary environment; 4) record keeping; 5) interactions with residents; 6) the difficulty of oral health care for frail residents; 7) bridging the gap between theory and practice; and 8) the emotional impact of the clinical experiences. Apparently, the students appreciated the opportunity to witness the complexity of care in a multidisciplinary context and to observe a practical program of oral health care. They described the rotations as unique and emotionally challenging but very worthwhile. Overall, they wrote positively about their experiences with the elderly residents, acknowledged the contribution of the rotation as important to their clinical maturation, and reported that the experience enhanced their appreciation of a dentist's professional responsibilities.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.992

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.437
Teacher spread0.406 · 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

Citations40
Published2005
Admission routes1
Has abstractyes

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