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Tackling Stress Management, Addiction, and Suicide Prevention in a Predoctoral Dental Curriculum

2014· article· en· W2242244861 on OpenAlexaff
Mario Brondani, Dhorea Ramanula, Komkhamn Pattanaporn

Bibliographic record

VenueJournal of Dental Education · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSeriousnessCurriculumStress managementMental healthMedical educationPsychologyAddictionStressorHarmMedicinePresentation (obstetrics)PedagogyClinical psychologyPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

Health care professionals, particularly dentists, are subject to high levels of stress. Without proper stress management, problems related to mental health and addiction and, to a lesser extent, deliberate self-harm such as suicide may arise. There is a lack of information on teaching methodologies employed to discuss stress management and suicide prevention in dental education. The purpose of this article is to describe a University of British Columbia Faculty of Dentistry module designed to address stress management and suicide prevention, using students' personal reflections to illustrate the impact of the pedagogies used. The module enrolls more than 200 students per year and has sessions tailored to the discussion of stress management and suicide prevention. The pedagogies include standardized patients, invited guest lectures, in-class activities, video presentation, and self-reflections. More than 500 students' self-reflections collected over the past five years illustrate the seriousness of the issues discussed and the level of discomfort students experience when pondering such issues. The instructors hope to have increased students' awareness of the stressors in their profession. Further studies are needed to unravel the extent to which such pedagogy influences a balanced practice of dentistry.

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)0.001
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 score0.075
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.024
GPT teacher head0.432
Teacher spread0.408 · 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

Citations33
Published2014
Admission routes1
Has abstractyes

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