Evaluation of the Implementation of Street Support Edinburgh in Response to the Predicted Increase in Homelessness in Edinburgh Following the COVID-19 Pandemic
Bibliographic record
Abstract
In response to the predicted increase in homelessness in Edinburgh, Scotland, following the Coronavirus disease of 2019 (COVID-19) pandemic, a ‘live’ digital resource, Street Support Edinburgh (SSE), was launched in the city in January 2021. SSE is a website and smartphone application run by Street Support Network (SSN), a registered charity, working online and offline, connecting and supporting local people and organisations to tackle homelessness. The resource is the first of its kind to be implemented in Scotland. This study aimed to assess the implementation of SSE in response to the predicted increase in homelessness in Edinburgh following the COVID-19 pandemic. A qualitative approach was taken to understand users’ experiences of SSE. The evaluation found a generally positive response to SSE from organisations in the homelessness field. We report on nine themes developed through analysis of the qualitative data: positive feedback on SSE; need for SSE resources; uses of SSE; joined up-working; user-friendliness of SSE; suggestions for alterations to SSE; COVID-19 and other implementation challenges; need/opportunities for promotion and engagement; potential wider rollout across Scotland. In conclusion, initial responses to the launch of SSE have been generally positive, but further awareness raising is likely required to increase SSE reach as in-person services and COVID-19-related protections reduce. Ongoing evaluation is required to track progress over time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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; a candidate call from one teacher head, not a consensus.
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".