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Record W2733229631 · doi:10.18192/uojm.v7i1.1993

Meeting the Challenges of an Aging Canadian Population: An Interview with Dr. Anna Byszewski, Patient Safety Advocate and Geriatrician at The Ottawa Hospital

2017· article· en· W2733229631 on OpenAlexaffvenueabout
Gaeun Rhee, Yuan Dong

Bibliographic record

VenueUniversity of Ottawa Journal of Medicine · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPolypharmacyGeriatricsMedicineDementiaCurriculumHealth careAged careNursingGerontologyMedical educationFamily medicinePsychologyPsychiatryPedagogyPolitical science

Abstract

fetched live from OpenAlex

Dr. Anna Byszewski, MD, is a geriatrician at The Ottawa Hospital and Regional Geriatric Program of Eastern Ontario. She completed medical school and residency training in Internal Medicine and Geriatric Medicine at the University of Ottawa. She is currently a full- time professor at the Faculty of Medicine at the University of Ottawa, an investigator at the Ottawa Hospital Research Institute, and is actively involved in developing and teaching communication, collaboration skills, and professionalism in the medical curriculum. Her remarkable dedication to improve the quality of care for geriatric patients through her teaching and practice were recognized in 2011 by The Ottawa Hospital Compass Award. As chair of a task group for the Dementia Network of Ottawa, she also produced the Driving and Dementia Toolkit that aims to improve quality of care and safety of geriatric patients with dementia behind the wheel. Her work is now an internationally recognized resource manual for health care workers, patients, and caregivers. In this interview, Dr. Byszewski highlights the important issues in improving the quality of care and safety for geriatric patients. This topic is of special importance due to the aging Canadian population and the unique challenges faced by health care providers such as reducing the risks of falls, cognitive decline, and polypharmacy. RÉSUMÉ Dre Anna Byszewski, MD, est une des gériatres principales à l’Hôpital d’Ottawa et au Programme gériatrique régional de l’est de l’Ontario. Elle a complété sa résidence en médecine interne et en médecine gériatrique à l’Université d’Ottawa. À l’heure actuelle, elle est professeure à temps plein à la Faculté de Médecine de l’Université d’Ottawa, chercheuse à l’Institut de recherche de l’Hôpital d’Ottawa, et est activement impliquée dans le développement et l’enseignement de la communication, des compétences de collabora- tion et du professionnalisme au niveau du curriculum médical. Son remarquable dévouement à l’amélioration de la qualité des soins pour les patients gériatriques, à travers son enseignement et sa pratique médicale, a été récompensé en 2011 par le Prix Compass de l’Hôpital d’Ottawa. En tant que présidente d’un groupe de travail du Réseau de la démence d’Ottawa, elle a également mis au point la Trousse d’information sur la conduite automobile et la démence, qui cherche à améliorer la qualité des soins et la sécurité au volant des patients gériatriques avec la démence. Son travail constitue désormais un manuel de ressources reconnu au niveau international, pour aider les travailleurs de la santé, les patients et les soignants qui gèrent de tels défis. Dans cette entrevue, Dre Byszewski met en évidence les questions importantes dans le domaine de l’amélioration de la qualité des soins et de la sécurité des patients chez les pa- tients gériatriques. Ce sujet est d’une importance particulière en raison de la population canadienne vieillissante et des défis uniques qu’affrontent les gériatres, tels que la réduction du risque de chutes et la polypharmacie.

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)0.014
metaresearch head score (Gemma)0.023
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.111
Threshold uncertainty score0.809

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0460.011
Scholarly communication0.0060.005
Open science0.0040.005
Research integrity0.0060.025
Insufficient payload (model declined to judge)0.0020.001

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 head0.118
GPT teacher head0.326
Teacher spread0.208 · 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
GenreOther

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
Published2017
Admission routes3
Has abstractyes

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