Evidence map›Paper›PMID 37350907›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2023

H4H: A Comprehensive Repository of Housing Resources for Homelessness.

Samue Osebe, Jack Tsai, Yu Hong

Abstract read
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Samue OsebeUniversity of Massachusetts Amherst.
Jack TsaiUniversity of Texas Health.
Yu HongUniversity of Massachusetts Amherst.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

More than half a million people were experiencing homelessness in America on any given night in 2021, yet only around 50% of them used shelters. To address unmet needs in homelessness, we report the creation of housing for homeless (H4H), the largest comprehensive repository of emergency shelters and other housing resources, from which we deployed state-of-the-art natural language processing approaches to extract information vital to individuals experiencing homelessness, including admission process, service provided, duration of stay, and eligibility. We frame information extraction as a question-answer task. Using 2,055 question-answer pairs for training and evaluation, the best performing system was a two-step classification and question-answering Roberta model with prompting, achieving a macro-average of 75.83 for F1 score. H4H and the annotated entries are publicly available as a benchmark dataset.

Identifiers

PMID37350907
PMCPMC10283121

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.