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Record W2042343617 · doi:10.1353/hpu.0.0070

Infectious Disease Exposures and Contact Tracing in Homeless Shelters

2008· article· en· W2042343617 on OpenAlex
Stephen W. Hwang, Alex Kiss, Minnie Ho, Cheryl S. Leung, Adi V. Gundlapalli

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Health Care for the Poor and Underserved · 2008
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsInstitute for Clinical Evaluative SciencesUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsOutbreakContact tracingEnvironmental healthCommunicable diseaseMedicineDemographyPublic healthGeographyInfectious disease (medical specialty)DiseaseSocioeconomicsCoronavirus disease 2019 (COVID-19)VirologyNursing

Abstract

fetched live from OpenAlex

An outbreak among homeless shelter users of a communicable disease with a short generation time would pose serious public health challenges. Data from Toronto were used to examine the number of shelter residents potentially exposed in the event of such an outbreak. A shelter user had contact with a mean of 97 other residents (range, 1-292) in one day and a mean of 120 (range, 2-624) in eight days. After a single week, contact tracing becomes difficult due to the challenge of locating homeless people who have left the shelter system. Over an 8-day period, individuals who used more than one shelter had contact with an average of 98 more other shelter residents than those who stayed in a single shelter had. At the onset of a serious outbreak, it may be desirable to institute policies that strongly encourage individuals to remain at their current shelter for the duration of the outbreak.

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.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.066
GPT teacher head0.387
Teacher spread0.321 · 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