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Developing and Evidence-Based Palliative Care Curriculum for Neurology Resident Trainees (P6.270)

2016· article· en· W2581318452 on OpenAlexaffabout
Jonathan Yeung Laiwah, Amrita Sarpal, Valerie Schulz, Teneille Gofton

Bibliographic record

VenueNeurology · 2016
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsWestern UniversityLondon Health Sciences Centre
Fundersnot available
KeywordsCurriculumNeurologyPalliative careMedicineMedical educationFamily medicinePsychologyPsychiatryNursingPedagogy

Abstract

fetched live from OpenAlex

Objective: To develop an evidence-based palliative care curriculum designed to provide neurology residents with the general palliative care skills required for providing patient care along the continuum of life. Background: Graduating neurology residents require general palliative care skills. In Canada, there is currently no curriculum designed specifically for neurology residents. Methods: A needs assessment of the palliative skills necessary for a neurology resident was performed. Focus groups were held with physicians, allied health care and senior residents. Semi-structured interviews were held with patients and their caregivers. Interviews analysed using qualitative thematic analysis techniques. A systematic search of the current literature was performed. Further, the Kolb learning style inventory will be used to determine the learning style of neurology residents, which will subsequently inform the type and design of targeted educational modules specific to neurology residents. Results: Qualitative analysis identified 3 overarching challenges for neurology residents: 1) uncertainty regarding disease trajectory in neurology and timing of palliative care discussions; 2) cohesiveness of the health care team regarding end of life issues; 3) the role of the resident in initiating palliative care. General principals identified for inclusion were: symptom management, communication, psychosocial aspects of care, care coordination and access, and myths and pitfalls in palliative care. Additional principals specific to neurological populations included: understanding the trajectory of neurologic disease and how it differs from other medical illness, and discontinuation of non-invasive ventilation among other topics. Conclusions: This project will identify the current best evidence and expert opinion in palliative care neurology. The data will be used to develop a novel Canadian neurological palliative care curriculum.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.004

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 head0.181
GPT teacher head0.419
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations0
Published2016
Admission routes2
Has abstractyes

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