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Record W4388906205 · doi:10.1080/01488376.2023.2282639

Services for Homeless Youth during COVID-19: The Case of a Canadian Community

2023· article· en· W4388906205 on OpenAlexaffabout
Jordan Babando, Shirley Chau, John R. Graham, Stephanie Laing, Danika A. Quesnel, Jamie Lloyd-Smith

Bibliographic record

VenueJournal of Social Service Research · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsPenticton Regional HospitalOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaUniversity of TorontoLaurentian University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Social workPsychologySociologyPolitical scienceMedicineVirologyOutbreak

Abstract

fetched live from OpenAlex

This exploratory study aimed to understand how the COVID-19 pandemic has affected a medium-sized community’s frontline workers in the Canadian youth homelessness services sector. This study phenomenological case study elicited practitioner knowledge and experience in servicing homeless youth in a medium-sized community – Kelowna, British Columbia, Canada. Two in-depth focus groups were conducted with a convenience sample of participants (N = 9). Thematic analysis revealed five overarching themes: a) Community Connection, b) COVID-19 Challenges and Services, c) Provider Well-being, d) Successes, and e) Youth Services and Housing. The results illustrate the early impact of COVID-19 on service providers and provision for youth experiencing homelessness, and the adaptations needed to provide them with services during this time. Replication of this research into other regions and social services is recommended. Future research that provides a retrospective account would offer a valuable point of comparison of providing social services to homeless youth during and after COVID-19 public restrictions.

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)0.012
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0090.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.361
GPT teacher head0.552
Teacher spread0.191 · 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 designQualitative
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
Published2023
Admission routes2
Has abstractyes

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