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Record W4383710140 · doi:10.1017/s0714980823000235

Understanding the Patient Experience of Foreign-Born Older Adults: A Scoping Review of Older Immigrants Receiving Health Care in Canada

2023· review· en· W4383710140 on OpenAlexaffabout
Jessica Wood, Paul Stolee, Catherine Tong

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2023
Typereview
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsUniversity of WaterlooMcMaster University
Fundersnot available
KeywordsImmigrationHealth careGerontologyForeign bornMedicineMental healthPopulationNursingPsychologyPolitical sciencePsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

In Canada, foreign-born older adults (FBOAs) have a higher prevalence of chronic conditions and poorer self-reported physical and mental health than their Canadian-born peers. However, very little research has explored FBOAs' experiences of health care after immigration. This review aims to understand the patient experiences of older immigrants within the Canadian health care system. Employing Arksey and O'Malley's framework for scoping reviews, we searched six databases and identified 12 articles that discussed the patient experience of this population. Although we sought to understand patient experience, the studies largely focused on barriers to care, including: communication difficulties, lack of cultural integration, systematic barriers in health care, financial barriers, and intersecting barriers related to culture and gender.This review provides insight into new areas of research and advocates for strengthened policy and/or programming. Our review also highlights that there is a paucity of literature for an ever-growing segment of the Canadian population.

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)0.006
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.563
Threshold uncertainty score0.869

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0100.016
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.087
GPT teacher head0.369
Teacher spread0.282 · 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 designSystematic review
Domainnot available
GenreReview

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
Published2023
Admission routes2
Has abstractyes

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Same venueCanadian Journal on Aging / La Revue canadienne du vieillissementSame topicPatient Satisfaction in HealthcareFrench-language works237,207