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Record W4300960928 · doi:10.1080/10530789.2022.2127876

The provision of dental care to emergency shelter users corresponds to lower shelter use over time among typical users

2022· article· en· W4300960928 on OpenAlexaffabout
Ali Jadidzadeh, Luke Duignan, Daniel J. Dutton

Bibliographic record

VenueJournal of Social Distress and the Homeless · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsDalhousie UniversityUniversity of Calgary
Fundersnot available
KeywordsReceiptDental carePropensity score matchingMedicineIntervention (counseling)CohortMedical emergencyFamily medicineNursingBusiness

Abstract

fetched live from OpenAlex

Using linked data from emergency shelters and a dental program in Canada, we sought to determine whether dental care is associated with fewer emergency shelter stays in this retrospective cohort study. We can observe when an individual accessed free dental care and how emergency shelter use changed over four years of follow-up, matching participants to comparable controls. We estimate difference-in-differences effects for each year since receipt of dental care. We estimate models for the typical user (M-estimation) and the average user (OLS regression). We found that in years 2, 3, and 4 after care, the typical user experienced a net result of fewer shelter stays than controls. The estimated average user stayed more nights in shelter than controls over the four years after dental care, likely driven by outliers that used substantially more shelter stays than the typical user. These results are consistent with the idea that participants benefit in the long-term from dental care received while at an emergency shelter after an initial stabilization period. That is likely due to both the permanent nature of the intervention and the lack of access to publicly-funded dental care in Canada.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.999

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 studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.344
Teacher spread0.329 · 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.

Study designObservational
Domainnot available
GenreEmpirical

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
Published2022
Admission routes2
Has abstractyes

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