It Takes a Whole Community: A Pragmatic, Strength-based Needs Assessment of Programs and Services Addressing Youth Homelessness in Bruce and Grey Counties, Ontario, Canada
Bibliographic record
Abstract
This dissertation focuses on the problem of rural youth homelessness in southwestern Ontario, Canada. Traditionally, homelessness has been characterized as an urban problem, but over the last 15 years a growing amount of research has shown that, while urban environments are far more populous and homelessness far more visible in these areas, the problem is equally pervasive in rural and remote regions, however differently it might manifest itself. Only a handful of studies exist in Canada on rural youth homelessness, and currently there are none that explore solutions to this problem in a rural context. \n \nThe study presented here was conducted in Bruce and Grey Counties, Ontario, Canada, between 2017-19, and has been divided into two parts based on two different phases of research. The first part presents the results of a homeless enumeration consisting of a period prevalence count (PPC) conducted across both counties between April 23-27, 2018 in order to provide a demographic snapshot of the region's homeless population. This study was the first of its kind to be conducted in this region. The second part presents the results of a strength-based community needs assessment that was conducted following the enumeration to determine the extent and quality of programs and services addressing youth homelessness in the two counties. Using theoretical principles borrowed from American pragmatism, and a grounded approach to methodology, I argue that emergency housing for youth and mental health services should be the focus of systems change in the Counties, and offer ways that this can be done that build on the cultural assets possessed by rural communities.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".