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Record W4292534087 · doi:10.5206/ijoh.2022.2.14773

“We actually came to a point where we had no staff”: Perspectives of Senior Leadership in Canadian Homelessness Service Providers During COVID-19

2022· article· en· W4292534087 on OpenAlexaffvenueabout
Stephanie Campbell, Chelsea Noël, Ashley Wilkinson, Rebecca Schiff, Jeannette Waegemakers Schiff

Bibliographic record

VenueInternational Journal on Homelessness · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of CalgaryLakehead University
Fundersnot available
KeywordsPreparednessPandemicQualitative researchPublic relationsService providerCoronavirus disease 2019 (COVID-19)Human servicesService (business)Political sciencePsychologySociologyBusinessMedicineMarketing

Abstract

fetched live from OpenAlex

Canadian homeless service providers (HSPs) serve individuals with complex needs. COVID-19 has introduced additional challenges to service provision leading to increased pressure on the organizations and the staff they employ. Little is known about how Canadian HSPs were affected by the global pandemic. The current research sought to address this knowledge gap by using qualitative research methodology to assess leaders’ perception of staff well-being and identify specific organizational challenges associated with the COVID-19 pandemic. 42 semi-structured interviews were conducted with senior leadership in Canadian HSPs, including Program Directors, Executive Directors, CEOs, key department managers and program coordinators. Although unanticipated, themes relating to positive outcomes and growth were also identified. Qualitative analysis revealed four overarching themes within the data: Organizational Challenges, Individual Challenges, Organizational Opportunities and Individual Opportunities. Implications of this study include informing pandemic preparedness in the homelessness sector and reducing occupational risks for staff.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.074
GPT teacher head0.380
Teacher spread0.306 · 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

Citations5
Published2022
Admission routes3
Has abstractyes

Explore more

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