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Record W4200188092 · doi:10.1108/hcs-08-2021-0023

The impact of COVID-19 on research within the homeless services sector

2021· article· en· W4200188092 on OpenAlexafffundabout
Jeannette Waegemakers Schiff, Eric Paul Weissman, Deborah M. Scharf, Rebecca Schiff, Stephanie Campbell, Jordan Knapp, Alana Jones

Bibliographic record

VenueHousing Care and Support · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsLakehead UniversityUniversity of New BrunswickUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsData collectionPandemicCoronavirus disease 2019 (COVID-19)Work (physics)OriginalityPublic relationsPsychosocialScale (ratio)DistancingBusinessPolitical sciencePsychologySociologyMedicineQualitative researchGeographyEngineering

Abstract

fetched live from OpenAlex

Purpose This paper aims to discuss the challenges of conducting research with homelessness services frontline workers during the COVID-19 pandemic. Design/methodology/approach Between 2015 and 2019, the research team surveyed frontline staff in three cities about their psychosocial stressors and needs. In 2020, the authors replicated the previous study and expanded data collection to seven cities across Canada to determine the extent to which the COVID-19 pandemic impacted the well-being of frontline staff. This report describes how the authors adapted the research methodologies to continue work throughout the pandemic, despite various restrictions. Findings The original studies had very high participation rates because of several methodological approaches that minimized barriers, especially in-person data collection. During the pandemic, distancing requirements precluded replication of these same methods. Research strategies that enabled staff participation during working hours, with designated time allotted for participation, was key for ensuring high participation rates, as access to technology, availability of free time and other factors frequently make online survey research a hardship for these staff. Restrictive interpretation and regional variations of COVID-19 guidelines by some research ethics boards were also a challenge to rapid and responsive data collection. Originality/value Few studies describe the experiences of frontline workers in the homelessness sector, and quantitative reports of their experiences are particularly scant. Consequently, little is known about specific methodologies that facilitate large-scale data collection in the homelessness services sector. The present research advances the field by providing lessons learned about best practice approaches in pre and post COVID-19 front line worker contexts. A strength of this research is the well-controlled design. The authors collected data within several of the organizations that had previously participated. This fortunate baseline provided opportunity for comparison before and during the pandemic; the authors can highlight factors that might have had influence during the pandemic.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.124
GPT teacher head0.505
Teacher spread0.381 · 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

Citations7
Published2021
Admission routes3
Has abstractyes

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