Excellence in Homelessness Services: Evidence-Based Frontline Practices
Bibliographic record
Abstract
Optimizing the performance of frontline workers in homelessness services (FWHSs) is vital for delivering effective support to individuals experiencing homelessness while prioritizing worker wellness. This paper builds on a previous study applying Orlick's "Wheel of Excellence" mental success elements, originally based on research with Olympic athletes, to social services. It assessed how high-performing FWHSs used these elements, providing practical insights for tailored mental-readiness training in social service facilities. The success elements—commitment, self-belief, positive imagery, mental preparation, full focus, distraction control, and constructive evaluation—were evident in FWHSs when successfully navigating challenging situations. This study provides further analysis of the elements contributing to FWHS’s mental readiness, including quotations from “excellent” FWHSs to highlight their common best practices. It defines excellence in frontline homelessness services, illustrates the use of the seven success elements, and identifies performance blocks and coping strategies from a frontline perspective. Performance blocks include general stressors and mistakes by trainees. The accompanying motivational quotes from high performers are akin to storytelling, elevating understanding and facilitating learning. Recommendations advocate tailoring existing tools, creating online learning opportunities, and implementing an Indigenous-inspired Sharing Circle for evidence-based practice discussions in homelessness services.
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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 | 0.031 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".