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Record W2410042631 · doi:10.1177/082585970001600302

Family Medicine Residents’ Knowledge and Attitudes about End-of-life Care

2000· article· en· W2410042631 on OpenAlexaffabout
Fred Burge, Paul McIntyre, David Kaufman, Gerri Frager, Ann Pollett

Bibliographic record

VenueJournal of Palliative Care · 2000
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsCape Breton Regional HospitalGrace (Canada)Queen Elizabeth II Health Sciences CentreIzaak Walton Killam Health CentreDalhousie University
Fundersnot available
KeywordsEnd-of-life careFamily medicinePalliative careNursingMEDLINEMedicineTerminal carePsychologyGerontologyPolitical science

Abstract

fetched live from OpenAlex

The medical management of end-of-life symptoms, and the psychosocial care of the dying and their families have not been a specific part of the curriculum for undergraduate medical students or residency training programs. The purpose of our research was to assess family medicine residents' knowledge of and attitudes toward care of the dying. All entering (PGY1) and exiting (PGY2) residents of the Dalhousie University Family Medicine Residency Program were given a 50-item survey on end-of-life care. They survey contains two 25-item subscales concerning attitudes/opinions toward end-of-life care, and knowledge about care. Thirty-one of the 33 entering PGY1s 94%) and 26 of the 30 exiting PGY2s (86%) completed the surveys. Overall attitude scores were felt to be high among both groups, with little difference between them. Areas of concern regarding the adequacy of knowledge were found in relation to managing opioid drugs and the symptom of dyspnea. Interventions are now in development to address these issues in the residency program. In an era of subspecialties, the challenge of integrating these areas into the curriculum without creating rotations in specialist palliative care is an issue faced by most family medicine residency programs.

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)0.001
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 score0.229
Threshold uncertainty score0.852

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.125
GPT teacher head0.441
Teacher spread0.316 · 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
Published2000
Admission routes2
Has abstractyes

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