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Death, dying and bereavement: a survey of dental practitioners

2008· article· en· W1978437695 on OpenAlexaffabout
Hagen Klieb, Michael Wiseman

Bibliographic record

VenueSpecial Care in Dentistry · 2008
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsHôpital Saint-LucUniversity of Toronto
Fundersnot available
KeywordsMedicineSympathyGriefDental educationFamily medicineCurriculumContinuing educationFormal educationPsychiatryDentistryPsychologyMedical educationPedagogy

Abstract

fetched live from OpenAlex

The dentist's role following the death of a patient in his/her practice has received little attention in the literature. This study determined the prevalence of death within a dental practice. It also investigated methods by which dentists supported grieving survivors, and how frequently dentists received formal bereavement education in dental school. A perceived need for training in death and dying was also investigated. A survey was mailed to 200 randomly selected general dental practitioners in Ontario, Canada. It was found that (1) the vast majority of respondents (86%) had experienced the death of a patient within the past 12 months; (2) support methods included sending sympathy cards (79.3%), sending flowers (34.5%), attending the funeral or wake (23%), or visiting/calling survivors (11.5%); (3) only 4% of respondents reported receiving formal bereavement education during dental school; and (4) 61% believed bereavement education should be included in the dental school curricula. While the majority of dentists in this study provided bereavement support and believed they could effectively comfort grieving persons, these dentists experienced significant stress when dealing with issues of death and bereavement. The stress may be explained by inadequate education and exposure to the issues of death and dying.

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 score0.028
Threshold uncertainty score0.998

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)0.0010.000

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 head0.057
GPT teacher head0.362
Teacher spread0.305 · 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

Citations7
Published2008
Admission routes2
Has abstractyes

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