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Record W3049493238

Caring for patients with lived experience of homelessness.

2020· review· en· W3049493238 on OpenAlexaff
Anne Andermann, Gary Bloch, Ritika Goel, Vanessa Brcic, Ginetta Salvalaggio, Shanell Twan, Claire Kendall, David Ponka, Kevin Pottie

Bibliographic record

VenuePubMed · 2020
Typereview
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsInstitute of Health EconomicsUniversity of British ColumbiaCollege of Family Physicians of Canada
Fundersnot available
KeywordsData scienceWorld Wide WebComputer scienceMedicine
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To guide family physicians working in a range of primary care clinical settings on how to provide care and support for patients who are vulnerably housed or experiencing homelessness. SOURCES OF INFORMATION: The approach integrates recommendations from evidence-based clinical guidelines, the views of persons with lived experience of homelessness, the theoretical tenets of the Patient's Medical Home framework, and practical lessons learned from family physicians working in a variety of clinical practice settings. MAIN MESSAGE: Family physicians can use simple and effective approaches to identify patients who are homeless or vulnerably housed; take initial steps to initiate access to housing, income assistance, case management, and treatment for substance use; and work collaboratively using trauma-informed and anti-oppressive approaches to better assist individuals with health and social needs. Family physicians also have a powerful advocacy voice and can partner with local community organizations and people with lived experience of homelessness to advocate for policy changes to address social inequities. CONCLUSION: Family physicians can directly address the physical health, mental health, and social needs of patients who are homeless or vulnerably housed. Moreover, they can champion outreach and onboarding programs that assist individuals who have experienced homelessness in accessing patient medical homes and can advocate for broader action on the underlying structural causes of homelessness.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score0.899

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
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 head0.145
GPT teacher head0.408
Teacher spread0.263 · 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 designNot applicable
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

Citations18
Published2020
Admission routes1
Has abstractyes

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