The impact of COVID-19 on research within the homeless services sector
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.513 | 0.560 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.023 | 0.051 |
| Scholarly communication | 0.032 | 0.023 |
| Open science | 0.009 | 0.039 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".